Artificial neural network and fuzzy logic based boiler tube leak detection systems

ABSTRACT

Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior. The second design philosophy integrates ANNs with approximate reasoning using fuzzy logic and fuzzy sets. In the second design, ANNs are used for learning, while approximate reasoning and inference engines are used for decision making. Advantages include use of already monitored process variables, no additional hardware and/or maintenance requirements, systematic processing does not require an expert system and/or a skilled operator, and the systems are portable and can be easily tailored for use on a variety of different boilers.

INTRODUCTION

1. The present invention in its most preferred embodiments relates tothe detection of leaks in boiler tubes; more particularly it relates tothe early detection of leaks in tubes of industrial type boilers tothereby allow the operators of such boilers, including utilities, toschedule a shut down for repair rather than suffer a forced outage whensuch leaks later become catastrophic; and still more particularly tosuch early detection of leaks to thereby significantly increase thechances of limiting damage to adjacent tubes in such boilers. Thepresent new and improved system utilizes an approach different fromthose heretofore taken and taught in the prior art. As utilized herein,there is effected the monitoring of a set of tube leak sensitivevariables, i.e. variables which exhibit significant changes whenever aleak occurs in a boiler tube. It will be appreciated, of course, thatwhen a tube starts to leak, the output values of these sensitivevariables start to change in response to that particular leak. Inaddition, in the approach utilized in the development of the instantinvention including the methods, techniques, and system comprising same,the approach has been to correlate more than one sensitive variable tosuch leak. Accordingly, by relying on different sources of informationabout a leak and by correlating a number of leak sensitive variables, ithas been found that the likelihood of early detection of such leaks isgreatly enhanced. As will be appreciated from a more detaileddescription infra, in the technique comprising the instant invention,one of the first tasks was to find a functional map between the changesin a plurality of such sensitive variables and the occurrence of a tubeleak, i.e. a multi-variable function whose parameters are the sensitivevariables and whose output is the tube leak level. Of course, classicalapproximation methods might be used to find this map as, for instance,by using the Weierstrauss theorem wherein a continuous function can beapproximated to an arbitrary degree of accuracy through the utilizationof classical techniques employing, for instance, a polynomial. However,for the very complex situation related to tube leaks in boilers, thereare several reasons why such classical approximation methods are notsuitable including, for instance, that such technique requires one toassume a priori form of the map, i.e. the degree of the polynomial inorder to approximate same. A further reason why such approximationmethods are not suitable is that extensive computer simulations haveshown that for high order polynomials, which would be the case in thepresent invention arena, the approximation of complex maps results innumerical instabilities which are encountered during the computation ofthe coefficients of the polynomial. Still another reason for not usingsuch a classical approach is that it is fraught with the difficulties ofbeing not easily implemented in computer hardware. On the other hand,the instant invention, in its simpler form, utilizes artificial neuralnetworks (ANN) to identify the complex map. Since ANNs are known to bemodel free approximaters one does not need to assume a priori form ofthe map and further computer simulations are easily and effectivelyutilized in both computer hardware and software. Accordingly, theinstant invention relates to the utilization of a plurality of ANNs todetect the presence of a tube leak as well as determine its location inthe boiler. Further, the instant technique utilizes a decentralizedarchitecture or structure for such networks. More specifically, a firstANN is utilized to make a relatively simple decision concerning thepresence of a leak. This first ANN is trained on what is herein referredto as universal leak sensitive variables (ULSV), which are sensitivevariables that respond to a leak in a boiler regardless of its locationtherein. Once such first ANN determines that there is indeed a leak inthe boiler, the next process step in the practice of the instantinvention is to utilize what is herein referred to as local leaksensitive variables (LLSV), rather than said ULSVs, which LLSVs are mostsensitive for a given location both along a tube and across the crosssection of the boiler. It has been found that there are a plurality ofcommon sensitive variables for designated locations in a boiler and thepresent invention utilizes the most sensitive thereof for a givenlocation wherein the presence of the leak is manifested by a change inthe same subset of such LLSVs. Accordingly, a plurality of dedicatedANNS are utilized in this second step to perform localized leakdetection for the location of such common sensitive variables. AlthoughANNs are known to be universal approximaters, they utilize data drivenapproaches which translates into performance acceptable for boilershaving similar characteristics. In other words, an ANN based systemalthough quite improved over heretofore prior art methods for earlydetection of tubes requires that after it is trained it be utilized onlyon similar type boilers. Since a principle object of the instantinvention is, at least in the more sophisticated embodiments thereof, toprovide a high degree of portability of the instant system wherein isrequired a minimum of tuning of same when it is used and moved from oneboiler to another, the more advanced embodiments of this inventionutilize the integration of fuzzy logic with such ANNs whereby isutilized available input-output information about tube leaks to build afuzzy map whose input is available, numerical, and linguistic tube leakinformation and whose output is characterization of the sensitivevariables. In this more sophisticated approach, there is utilizedinference engines to invert the resulting map and to render moreaccurate decisions about tube leaks in boilers. The decision makingprocedure utilized in the operation in these more sophisticatedintegrated systems has been found to be greatly implemented by the useof a set of “If Then” rules.

BACKGROUND OF THE INVENTION

2. The present invention relates generally to new, improved, andreliable systems, methods, and techniques for the detection of leaks inthe tubes of industrial boilers, including those of the types used byutilities to produce steam for electric power production.

3. Boiler Tube Leak Detection.

4. Because of heat, pressure, and wear over time, boiler tubeseventually begin to leak, i.e., the beginning of a “leak event.” When aboiler tube(s) starts to leak, steam which flashes over from the waterescaping through the leak therein is lost to the boiler environment. Ingeneral, the amount of leaked water/steam may be small at the inceptionof a tube leak event. However, unless the tube is repaired, the leakwill continue to grow, i.e., the tube leak rate increases with timeuntil the tube eventually ruptures. Once such rupture occurs the utilityoperating such boiler is forced to shut it down immediately.

5. Boiler tube failures are a major cause of forced shut downs in fossilpower plants. For example, approximately 41,000 tube failures occurevery year in the United States alone. The cost of these failures provesto be quite expensive for utilities, exceeding $5 billion a year. [Lind,M. H., “Boiler Tube Leak Detection System,” Proceedings of the ThirdEPRI Incipient-Failure Detection Conference, EPRI CS-5395, March 1987]

6. In order to reduce the occurrences of such forced outages, earlyboiler tube leak detection is highly desirable. Early boiler tube leakdetection would allow utilities to schedule a repair rather than tosuffer a later forced outage. In addition, the earlier the detection,the better the chances are of limiting damage to adjacent tubes.

7. Artificial Neural Networks.

8. Artificial neural networks (ANNs) are information-processing modelsinspired by the architecture of the human brain. ANNs are capable oflearning and generalization and are model-free adaptive estimators ofmaps (relations between the input and the output of the ANN, or, aslater referenced, an inference engine) which learn using example data.As is discussed in the prior art, including the patent literature, whena neural network is to be used in detection applications, it isnecessary to execute beforehand a learning procedure for establishingsuitable parameter values within the ANN. In the learning procedure, aset of sample patterns (referred to herein as the learning data), whichhave been selected in accordance with the patterns which are to berecognized, are successively inputted to the ANN. For each samplepattern there is a known appropriate output pattern, i.e. a patternwhich should be produced from the network in response to that inputpattern. The required known output patterns are referred to as theteaching data. In the learning procedure, the learning data patterns aresuccessively supplied to the ANN, and resultant output patterns producedfrom the ANN are compared with the corresponding teaching data patterns,to obtain respective amounts of recognition error. The internalparameters of the ANN are successively adjusted in accordance with thesesequentially obtained amounts of error, using a suitable learningalgorithm. These operations are repetitively executed for the set oflearning data, until a predetermined degree of convergence towards amaximum degree of pattern recognition is achieved (i.e., the maximumthat can be achieved by using that particular set of learning data). Thedegree of recognition can be measured as a recognition index, expressed,for example, as a percentage.

9. The greater the number of sample patterns constituting the learningdata, the greater will be the invariant characteristic information thatis learned by the ANN. Alternatively stated, a learning algorithm whichis utilized in such a procedure (i.e. for adjusting the ANN internalparameters in accordance with the error amounts obtained during thelearning procedure) attempts to achieve learning of a complete set ofprobability distributions of a statistical population, i.e. astatistical population which consists of data, consisting of all of thepossible patterns which the ANN will be required to recognize afterlearning has been achieved. That is to say, the learning algorithmperforms a kind of pre-processing, prior to actual pattern recognitionoperation being started, whereby characteristics of the patterns whichare to be recognized are extracted and applied to modify the internalparameters of the ANN.

10. In the practice of the prior art it has been necessary to utilize aslarge a number of sample data in the learning procedure as possible, inorder to maximize the recognition index which is achieved for a ANN.However, there are practical limitations on the number of samplepatterns which can be stored in memory for use as learning data.Furthermore, such learning data may include data which will actuallytend to lower the recognition index, if used in the learning procedure.Accordingly, and as will be better appreciated after reading andunderstanding the more detailed description below, the decentralizedarchitecture or structure of the instant new detection system and thestaging of testing significantly overcomes such prior art relateddisadvantages.

11. ANNs can be divided into two classes: feed-forward and feedbackneural networks. Within each class, ANNs are also characterized by thenumber of hidden layers, number of neurons in a given layer, and themethod of learning. While many different types of learning areavailable, the back propagation learning algorithm (BPLA) is of the mostinterest to the practice of the instant invention. The BPLA is anerror-correcting learning procedure which uses the gradient descentmethod to adjust the synaptic weights. BPLA is intended for ANNs with aninput layer, any number of hidden layers, and an output layer. In themost preferred embodiments of the instant invention, the ANNs used arefeed forward and possess two hidden layers. Other types of ANNs withdifferent topologies and learning algorithms can be used as well. Aswill be better appreciated from the teachings and discussions foundinfra, the first two embodiments of the instant invention, i.e.embodiments one and two utilize ANNs to effect the desired and necessarylearning and decision making for early detection of boiler tube leakevents.

12. Fuzzy Logic.

13. Exact models of dynamical systems become increasingly difficult toobtain if not impossible as system complexity increases. This fact issummarized by what Zadeh, infra, called the principle ofincompatibility: “as the complexity of a system increases, our abilityto make precise and yet significant statements about its behaviordiminishes until a threshold is reached beyond which precision andsignificance (or relevance) become almost mutually exclusivecharacteristics.” [L. A. Zadeh, “A theory of approximate reasoning,” inJ. Hayes, D. Michie, and L. I. Mikulich, (eds.), Machine Intelligence,Vol. 9, Halstead Press: New York, SMC-3, 1979]

14. The uncertainty in the knowledge about real-world systems and theirdynamic models has motivated the application of fuzzy set theory tohandle real world problems. [L. A. Zadeh, “Fuzzy algorithms,”Information and Control, Vol. 12, 1968] [D. Dubois and H. Prade, “FuzzySets and Systems: Theory and Applications,” Academic, Orlando, Fla.,1980] This motivation stems from the fact that fuzzy set theory providesa suitable representation of the uncertainty in system knowledge anddynamic models. In fuzzy set theory the reasoning in the face ofuncertain information, called approximate reasoning, employs fuzzy logicas a framework for uncertain information processing and inference. [R.R. Yager and D. P. Filev, “Essentials of Fuzzy Modeling and Control,”Wiley Interscience, New York, 1994] Fuzzy set theory is an approachuseful for presenting and utilizing linguistic “qualitative”descriptions in computerized inference which improves the potential tomodel human reasoning in an inexact and uncertain domain in cases wherestatistical information is not available. The concept of possibility maybe used to model the confidence level of various hypotheses by a numberbetween zero and one, where one may be the highest degree of confidenceand zero the lowest, or vice versa. In order to quantify inexactness,fuzzy set theory utilizes the notion of a membership function in termsof the level of confidence that a particular element belongs to aparticular fuzzy set. Given the complexity of boiler tube leak events,it will be appreciated by those skilled in this art that there existssubstantial motivation to utilize fuzzy logic in attempting to effectthe detection of boiler tube leaks at the earliest possible moment by atechnique which looks for an approximate or “fuzzy” map, between tubeleak events and the sensitive variables, supra, and thereafter utilizeapproximate reasoning for detecting the occurrence and location ofboiler tube leaks. Accordingly, the second two embodiments of theinstant invention, i.e. embodiments three and four, integrate ANNs andfuzzy logic to effect the desired early detection of boiler tube leaks.

DESCRIPTION OF PRIOR ART

15. In the prior art, three main techniques have been proposed to detectboiler tube leaks: acoustic based systems, mass balance, and adb hocexpert systems. In the acoustic based systems taught and disclosed inU.S. Pat. Nos. 3,831,561, Yamamoto et al., Aug. 27, 1974; 4,960,079,Marziale et al., Oct. 2, 1990; 4,979,820, Shakkotta et al., Dec. 25,1990; and 4,998,439, Shepard, Mar. 12, 1991, the principal idea is tolisten, using acoustic sensors, to the sound produced by the jet ofsteam attendant a tube leak, or, as oftentimes herein referred to, aleak event. The technical limitation to this approach is that oftentimes the sound produced by the tube leak is buried in the backgroundacoustical noise of the tube environment. Accordingly, early detectionof a leak is rather difficult because at the early stage of a leakbackground acoustical noise oftentimes masks or overrides the noiseassociated with the escaping steam. From a cost standpoint, the priorart technique oftentimes requires fifty or more acoustic sensors tocover the main parts of the boiler where a leak is most likely to occur.In addition, these sensors have to be maintained in proper operatingcondition thereby resulting in high attendant maintenance costs.

16. In the mass balance approach, used by some utilities, when a leakoccurs there results a dependent increase in the amount of make up flow(amount of water needed to replace the loss of water due to the leak).Such an increase in make up flow is used as an indicator of themagnitude of the leak. As in the acoustic system based approach, supra,when the leak is small, the make up flow is negligible and all butimpossible to discern. In addition, the mass balance approach is validonly when the boiler is operating in a steady-state operation. Mostoften, this particular requirement can not be met since boilers normallyoperate under constantly changing dynamical conditions caused byattendant control system operation and changes in load on the generatingequipment utilizing the boiler output. Most important, this technique isfraught with the paramount disadvantage that even when the occurrence ofa leak is detected it can not be used to locate a leak in such boiler.

17. The add hoc technique consists of detection of tube leaks using aso-called expert system approach. A major drawback and disadvantage ofthis approach is that it lacks universality. That it to say, it can onlybe used, after an extended time of modification of the design andtuning, to the specific boiler with which the expert person (the personwho provides the rules) is familiar. Because of its add hoc nature, thedevelopment cost of such techniques can prove to be prohibitive. Thelack of universality of the add hoc approach makes it even lessattractive than the two prior art approaches and techniques discussedabove.

SUMMARY OF THE INVENTION

18. The instant, new and novel approach is used to overcome the priorart problems heretofore associated with effective and early detection ofboiler tube leak events and is multifaceted. For instance, at the outsetof attempting to meet the principal objects of the instant invention, adependable technique for identifying boiler tube leak sensitivevariables was developed. Thereafter and once an occurrence of a tubeleak event was determined, the boiler tube leak detection problemdealing with its location was solved by learning the map between suchsensitive variables, the leak level, and the leak location. Suchlearning can be accomplished by the practice of two different mappingprocedures with the map between sensitive variables and tube leak beingtreated as either a crisp map or a fuzzy map. In the first embodiment ofthe instant invention the crisp map represents a larger number ofsensitive variables than does the crisp map utilized in the secondembodiment of this new and novel invention. This larger number ofvariables represents the greater number of transducers which arecurrently utilized for control of more modem boilers as opposed to thesmaller number of transducers utilized in the monitoring and control ofboilers of older design. In the practice of both these embodiments, i.e.one and two, ANNs were used to learn these maps via supervised training.Alternatively, in the practice of the third and fourth embodiments ofthe instant invention, the map between sensitive variables and a tubeleak is modeled as a fuzzy map. It has been determined that fuzzy setsand fuzzy logic may conveniently be used to capture this map in the formof “If Then” fuzzy rules. The parameters of the fuzzy sets in these “IfThen” rules are learned using a fuzzy ANN. Once the map is correctlylearned, when future measurements of the relevant sensitive variablesare input to the ANN based detection system, it will output the value ofthe map for the particular combination of the variables. If the value ofthe map is zero, no leak is present. However, once the output of the ANNis nonzero (above a given threshold) a leak is present in the boiler andits location must be identified. Accordingly, herein are described andtaught four embodiments designed for detection and localization ofboilers tube leaks or leak events in industrial boilers. The first twodesigns, or embodiments rely solely on ANNs, while the last two, throughthe utilization of inference engines, integrate ANNs with fuzzy logic.

OBJECTS OF THE INVENTION

19. It is therefore a principle object of the instant invention toprovide utilities and other users of industrial boilers with a new,improved and dependable system which is capable of effecting earlydetection of boiler tube leaks. The instant, new and novel system,method and technique, instead of requiring additional acoustic sensors,takes advantage of existing process variables which are already in placeand are being used for the purpose of boiler control and monitoring. Theinstant, new and novel systems can be custom tailored to any boilerwithout the need for input of a so-called human boiler expert. Thedesigns taught, described, and claimed herein employ, in their moreadvanced development stages, two different technologies. The first andsecond embodiments use ANNs and the third and fourth embodimentsintegrate both such ANNs with fuzzy logic. In addition, theimplementation of the first embodiment, supra, may be through theutilization of software designed specifically for use therein, althoughthere presently is contemplated the VLSI implementation of this firstembodiment, in the form of a VLSI chip.

20. Still further and more general objects and advantages of the presentinvention will appear from the more detailed description set forthbelow, it being understood, however, that this more detailed descriptionis given by way of illustration and explanation only, and notnecessarily by way of limitation since various changes therein may bemade by those skilled in the art without departing from the true spiritand scope of the present invention.

DESCRIPTION OF THE DRAWINGS

21. In order to more fully understand the manner in which theabove-recited and other advantages and objects of the instant inventionare obtained, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsthereof which are illustrated in the appended drawings. Understandingthat these drawings depict only typical embodiments of the invention andare not therefore to be considered limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings in which:

22.FIG. 1 is a logic flow chart representing the movement of the programlogic through the process of identification of leak sensitive variables.As may be appreciated, all the variables of the instant process arefirst input via line 101 into data acquisition block 102 oralternatively first storage block 102 wherefrom each variable inputtherefrom is moved via line 103 into variable compute block 104 whereinthe sensitivity, or more particularly, the sensitivity function of eachsuch variable is computed and evaluated. Subsequently, the resultingvalue for the sensitivity function of each particular variable is movedvia line 105 to comparison block 106 wherein it is compared to athreshold value. Still subsequently, each variable which does not passthe threshold test(s) is eliminated via line 107 whereas each variablewhich passes the threshold test(s) is moved via line 108 to secondstorage block 109. It will be appreciated that once the first set ofleak sensitive variables is collected in second storage block 109, eachsuch variable is examined from the sense of redundancy. Accordingly,each variable in second storage block 109 is subsequently moved via line110 to redundancy block 111 wherein any two variables which correlateand which contain similar information about a particular leak event aresubjected to a redundancy test wherefrom one thereof is eliminated vialine 112. This step is a necessary one in order to limit the number ofvariables which are to be used in the instant, new and novel leakdetection system so as not to adversely effect the performance of same.As will be noted in later discussion, up to this point all the decisionsmade by said instant leak detection system by means of the flow ofprogram logic, are data driven and are sans the requirement or need forany input of human judgment. Subsequently the independent sensitivevariables identified in redundancy block 111 are moved via line 113 tointerpretation block 114 wherein they are tested with input of humanjudgment to ensure that, indeed, they are physically dependent on thetube leak event. Thereafter those variables which do not pass thevalidation test in interpretation block 114 are eliminated via line 115,whereas those which pass the validation test are moved via line 116 tothird storage block 117 for later use in tube leak detection operations.The instant program logic flow described above may utilize anyconvenient transfer means to move the program logic from one block toanother. Accordingly, the references to line numbers, such as, forexample, line 116 is not to be construed in any limiting manner.Although not shown in FIG. 1, it will be readily appreciated by thoseskilled in the art that an additional information collection means orstorage block may conveniently be disposed between redundancy block 111and interpretation block 114, i.e. in line 113, so that the sensitivevariables which pass the redundancy test may be collected and moreconveniently later validated in the operation of interpretation box 114by human input thereto for making the necessary comparisons with suchchanges in those variables with the standard of principles ofthermodynamics and mechanics.

23. In FIG. 2, there is illustrated in block diagram format an ANN baseddetector system designed according to embodiment one of the instantinvention. As may be seen, universal leak sensitive variables (ULSV) areinput via line 210 to tube universal leak detection system (ULDS) block211. In the first stage of utilization of this instant, new and novelsystem, a determination is made as to whether or not a leak is present.Thus, and just for example, when the output via line 212 of ULDS block211 is zero, no leak is present in the boiler whereas when the output ofblock 211 is one, a leak is present. Thereafter, when a leak event isdeclared to be present in the boiler by such output from block 211, thenext step utilized in the practice of the instant, new system is todetermine the location of such leak in the boiler. This is accomplishedby the use of a plurality of ANN based local leak detection systems(LLDS) herein illustrated generally at 213 and identified generally aseleven blocks comprising ANNs LLDS 1 through LLDS 11. It will beappreciated that one of each of the ANNs comprising 213 is utilized torepresent each location in the boiler where it has been previouslydetermined that a leak is likely to take place. As may be furtherappreciated, the input to each LLDS is its corresponding set of localleak sensitive variables, i.e. herein shown as LLSV 1 through LLSV 11.The output from each of the eleven LLDSs represents a leak levelwherein, for example, a level of zero indicates no leak is present. Itshould be further appreciated that the decentralized structure of thissystem allows for each such of LLDSs to operate simultaneously sincethey operate in parallel, one from another. It will be still furtherappreciated that before such parallel activity may be utilized, each ofthe eleven ANNs must first be trained. Details on such ANN learning ortraining are discussed in the section below dealing with “Description ofthe Preferred Embodiments.”

24. In FIG. 3 there is illustrated the architecture used for the ANNsherein utilized for purposes of boiler tube leak detection. Each suchANN has a single input layer and a single output layer, and in the mostpreferred embodiment thereof, two hidden layers. The blocks identifiedas F1-F3 represent sigmoids of the ANN wherein training thereofcomprises adjusting the weights w1-w3 and the biases b1-b3 of the ANNuntil output Y matches input Yd. The value of biases b1-b3 are initiatedto “1.” Then the learning algorithm will adjust such bias as part of thelearning process. See, for example, Table 5, infra. As noted, the ANNarchitecture utilized in the most preferred embodiments of the instantinvention have a single input layer with two hidden layers and oneoutput layer. Preferably, the input layer comprises three neurons, sinceit has been determined that three ULSVs are sufficient for determiningif a leak event is occurring. If a greater or lesser number of ULSVs isdesired, the number of neurons in said input layer may be adjustedaccordingly. The first hidden layer preferably comprises forty neuronsand the second hidden layer twenty-four neurons. It will be appreciatedfrom a more detailed description infra that, since the single inputlayer has three inputs, w1 is represented by a 40×3 matrix, whereas w2may be represented by a 40×24 matrix, and w3 may be represented by a24×1 matrix, or more correctly a 24×1 vector. The corresponding biases,to wit, b1 may be represented by a 40×1 vector, b2 represented by a 24×1vector, and b3 represented by a single value.

25. In FIG. 4 there is a graphical depiction of an actual example of theoutputs of the instant LLDSs, i.e. LLDS 1 through LLDS 11, illustratedand discussed in FIG. 2, supra. It should be noted that in theparticular example and leak situation herein depicted that the valuesfor five of the LLDSs indicated an output leak level of about zero andare not discernible, whereas five of the other LLDSs had leak leveloutputs ranging between about one and four. The alarm level output ofLLDS 4 is shown, after about 300 points, i.e., about 25 hours, to reachalmost ten, and therefore was interpreted to be representative of theleak location. It will, of course, be appreciated by those skilled inthis art that the particular graphical illustrations in FIG. 4 are, forthe sake of simplicity, reproduction, and convenience, merely simulatedby the draftsman and are not a literal representation, nor to beconstrued as such of the graphs plotted during the actual testing.

26. In FIG. 5 there is depicted still another graphical illustration ofan actual leak detection activity wherein the boiler tube leak detectionsystem of the instant invention actually predicted a tube leak eventsome six days prior to the outage event necessitated when such leakdegraded and progressed to the stage wherein the boiler could no longerbe operated. Again, as in the description of illustration of FIG. 4,supra, the graphical depiction is herein simulated for the sake ofsimplicity, reproduction, and convenience and is not a literalrepresentation of the graphical data actually obtained.

27. In FIG. 6 there is illustrated the architecture of an ANN based tubeleak detection system comprising the second embodiment of the instantinvention. As will be appreciated from a more detailed description,infra, the first embodiment of the instant invention which is generallyillustrated in FIG. 2, supra, proved to be very suitable for tube leakdetection in modern industrial boilers wherein a large number of processvariables are inherently monitored. In embodiment one, eleven likelyboiler locations can be monitored with some twenty-three processsensitive variables being required by that system, i.e., three ULSVs andtwenty LLSVs. Subsequent to confirming the use of the reliability ofsaid first embodiment, the development of the instant invention wasdirected to an early tube leak detection system more suitable for use inolder boilers where a considerable lesser number of variables aremonitored because of the considerably less instrument sophisticationassociated therewith. Accordingly, in embodiment two of the instantinvention, the ANN based detection system was conveniently arrangedwhereby the likely locations of a tube leak in such older boilers wasdivided into four groups or four subsystems. The occurrence or presenceof a leak is monitored for each of these groups and the detection in agroup is dependent on the instant new concept built around group leaksensitive variables (GLSV) which are herein shown as GLSV 1 through GLSV4 which are inputted into the four systems represented generally at 612and comprise four group leak detection systems (GLDS), i.e., GLDS 1through GLDS 4: economizer, waterwall, superheater, and reheater. Theoutput of the four subsystems comprising GLDS 1 through GLDS 4, aredelivered in parallel to inference engine 613, wherefrom, for instance,a level of zero indicates no leak present in a particular group. It willbe appreciated that the procedure used herein for identification of thedifferent variables utilizes the procedure taught for operation of thelogic flow chart illustrated and discussed in FIG. 1, supra, except thatinstead of the threshold value used for either of the ULSVs or the LLSVscompared in comparison block 106, a threshold value specific to GLSVs isutilized. Once inference engine 613 declares a leak to be present in aparticular group, via output line 614, the other groups will not befurther considered, i.e., only that particular group will be furtheranalyzed. It should, of course, be appreciated that each subsystem in aparticular group is in turn divided into a set of subgroups. Forexample, the superheater is divided into the primary superheater (SGL11) and the secondary superheater (SGL 12). Likewise, the reheater isdivided into a set of subgroups comprising the primary reheater (SGL 41)and the secondary reheater (SGL 42). Since the mechanics andconstruction of the economizer and the waterwall are such that thelocation of a leak event occurring is readily ascertainable once ingressthereto is effected, as for example, for purposes of observation orcorrection, it has been determined that these two areas of the boiler donot require further subdivision thereby resulting in a situation whereina group designated as GL 1 and the subgroup designated as SGL 1 for theeconomizer and wherein the group designated as GL 2 and the subgroupdesignated as SGL 2 for the waterwall are one and the same. To identifythe location of the leak within that particular subgroup, the concept ofsubgroup leak sensitive variables (SGLSV) is used as input to 615 wherethe identification of the leaky subgroup is identified.

28. More specifically, similar to the determination of the GLSVs in theprocedure taught above in conjunction with FIG. 1 and 6, there is alsodetermined by employment of similar procedure, a set of SGLSVs, whereinstill a different threshold value is utilized therefore in the operationof comparison block 106 than was the threshold value employed duringdetermination of such GLSVs. In the most preferred arrangement, arelatively small number, usually an average of two of said SGLSVs isassociated with each of said four boiler subsystems. To determine whichsubgroup contains the leak, the SGLSVs of each subgroup are input toANNs previously trained for handling such input and wherefrom there isoutput to a subgroup inference engine contained in 615 wherein isdetermined the particular location of the leak event.

29. From the above, it should now be appreciated that in the embodimentillustrated in FIG. 6, it is most desirably applied to the detection ofboiler tube leak events occurring in vintage, i.e. older style boilerswhich are not equipped with the more modern-day means for monitoring,detecting, and otherwise transducing a plethora of different eventstherein. The arrangement in the most preferred embodiment thereofcomprises first breaking down parts of the boiler into a number ofgroups and then ascribing to or further breaking down each such groupinto subgroups. In this most preferred embodiment, each group comprises,respectively, either the economizer, the waterwall, the superheater, orthe reheater, whereas two of such groups are further broken down into orfurther divided into subgroups comprising each of two locations and theother two groups are not so further divided. Accordingly, the net resultmay be more easily illustrated by the open tablulation below, wherein itwill be seen that the superheater and the reheater are broken down,respectively, into two subgroups comprising the primary superheater andthe secondary superheater and the primary reheater and the secondaryreheater, and further wherein the net result is four groups and sixsubgroups.

30. Economizer (GL 1):

31. 1. Economizer (SG 1)

32. Waterwall (GL 2):

33. 1. Waterwall (SG 2)

34. Superheater (GL 3):

35. 1. Primary Superheater (SGL 31)

36. 2. Secondary Superheater (SGL 32)

37. Reheater (GL 4):

38. 1. Primary Reheater (SGL 41)

39. 2. Secondary Reheater (SGL 42)

40. The SGL locations are detected using SGLSVs. Note that inuncaptioned Table 1, below, the SGLSV numbers are associated with theSGL location numbers. TABLE 1 SGLSV 11 SGLSV 12 SGLSV 2 SGLSV 3 SGLSV 41SGLSV 42 PBPGX (14) PBPGX (13) WDIWS PBPGX (14) PBPGX (6) PBPGX (6)PBPGX (13) PBPGX (11) PDA LFNTLTS (1) PBPGX (4) PBPGX (7) FFD (1) PBPGX(14) CHWMUVO PPRX (2)

41. As far as the leak detection is concerned; for every GL there is oneANN based GLDS. Given that a leak is detected in a given group, thedetection effort will then focus on that group to isolate the locationof the leak. For every subgroup of likely leak locations, within thatgroup which is thusly focused upon an ANN detection system is used todetect the leaky subgroup. It will be appreciated that in this design inthe most preferred embodiment thereof, an ANN based detection system ordetector will be trained and utilized for each of the areas comprisingthe four main groups. Likewise, an ANN based detector system mostpreferably will be trained for each of the six subgroups comprising SG11, SG 12, SG 2, SG 3, SG 41 and SG 42, supra. Two of the six are dualpurpose, i.e., the economizer (GL 1) and the economizer (SGL 1) areidentical and waterwall (GL 2) is identical with the detection systemcomprising the waterwall (SGL 2). Accordingly, although there are fourdetectors acting for the first group and six for second subgroup, thereare in reality a total of eight detectors. As will be appreciated fromdiscussions, supra, touching on the implementation of the instantembodiment by substituting therefore inference engines to do the work ofthe ANN based detection systems, the same quantitative relationship willhold, i.e., instead of four ANN based GLDSs and six ANN based subgroupleak detection systems (SGLDS) of embodiment two, the more sophisticatedversion and derivative thereof comprising embodiment four will mostpreferably employ four group leak inference engines (GLIE) and sixsubgroup leak inference engines (SGLIE). In either of these embodiments,it will be further appreciated that the number of GLSVs comprising GLSV1 through GLSV 4 most preferably totals eleven for this design.

42. Referring to FIGS. 7 and 8 collectively, therein are illustrated theneural fuzzy based detection systems comprising, respectively,embodiments three and four of the instant invention. More specifically,in regards to FIG. 7, it will be appreciated that embodiment three ispatterned after the architecture of embodiment one shown in FIG. 2,supra, wherein the same sensitive process variables information forembodiment one are inputted herein into embodiment three. However, theANN detectors utilized in embodiment one (to wit, those comprising LLDS1 through LLDS 11, as the LLDS generally at 213) are replaced in thisembodiment three by a plurality of leak detection inference engines(LDIE) comprising LDIE 1 through LDIE 11, and are shown generally at713. Contrary to the crisp ANN inference engine used in embodiments oneand two, supra, the inference engine used in these embodiments three andfour is a fuzzy decision maker, i.e., its input and outputs comprisefuzzy variables or qualities linguistically often expressed as, forexample, small, medium, and large. To make its decision, an inferenceengine uses fuzzy logic with its fuzzy knowledge base. The parametersinvolved in the fuzzy knowledge base are learned using ANNs.

43. In the ANN based boiler leak detection system comprising embodimentsone and two of the instant invention, the output of each ANN detectionsystem is a crisp signal. The inference engine utilized in operativeassociation therewith is a classical expert system which looks at theoutput of these detection systems (see, for example, the depictionscomprising FIGS. 2 and 6) and decides which output corresponds to theleak event.

44. In the fuzzy logic based boiler tube leak detection systemcomprising embodiments three and four of the instant invention, the ANNdetection systems are replaced with inference engines, as shown, forexample as LDIE 1 through LDIE 11 in FIG. 7 and as GLIE 1 through GLIE 4in FIG. 8. An inference engine comprises a fuzzy inference system whichis made up of three components: a knowledge base, which contains a setof fuzzy rules describing the fuzzy map between sensitive variables andthe leak; a data base which defines the membership functions used in thefuzzy rules, and a reasoning mechanism, which performs the inferenceprocedure upon the rules and given facts to make a conclusion about thepresence of a leak and its size. Because the output of each inferenceengine is fuzzy, the inference engine is a fuzzy expert system whichlooks at the output of each inference engine and determines which outputcorresponds to the leak event. For more detailed information and detailon such fuzzy inference systems, the reader's attention is directed toYager, R., Essential of Fuzzy Modeling and Control, John Wiley, 1994;Terano, T., Applied Fuzzy Systems, AP Professional, 1989; and, Kosko, B.Fuzzy Engineering, Prentice Hall, 1997, the teachings and disclosures ofwhich are hereby incorporated by reference thereto.

45. It will be appreciated that each of the LDIEs, shown generally andcollectively at 713, uses as a knowledge base, a fuzzy map between aleak in a given location and the set of appropriate sensitive variables,with said map being modeled by a set of “If Then” rules. Accordingly,when tube ULDS block 711 (most preferably, a first inference engine)receives input of ULSVs via line 710, it produces a fuzzy output whichherein, for example, was conveniently in the form of zero, small,medium, or large for the first stage determination of whether or not aleak is present in the boiler. As structured in the instant invention,when such output is either medium or large, the occurrence of a leakevent in the boiler is declared and all of the LDIEs comprising, asshown, eleven separate second inference engines, LDIE 1 through LDIE 11,respectively, and generally shown at 713 begin to estimate the locationof the leak with their outputs being sent in parallel to inference block714, i.e., the third inference engine, for final determination by outputvia line 715, said determination being effected in a fashion similar tothe determination discussed in FIG. 2, supra.

46. In the architecture of the instant, new and novel fourth embodimentof the instant invention as represented in FIG. 8, the same GLSVs, i.e.,GLSV 1 through GLSV 4, which were utilized in the practice of the secondembodiment of the instant invention and referred to in the discussion ofFIG. 6, supra, are inputted to a plurality, herein shown as fourseparate GLIEs comprising GLIE 1 through GLIE 4 and generally located at812, rather than to the ANN based GLDSs as utilized in the secondembodiment of the instant invention and depicted in FIG. 6, supra. As inthe practice of the first step of the instant second embodiment anoutput level from each of said four separate GLIEs is input to secondgroup inference engine 813 via line 814 wherein a level of zeroindicates no leak is present in any particular group. Again, as in orsimilar to the practice employed in the second embodiment of the instantinvention, it will be appreciated that the procedure used herein foridentification of the different variables utilizes the procedure taughtfor operation of the logic flow chart illustrated and discussed in FIG.1, supra. Once second group inference engine 813 declares a leak to bepresent and identifies the particular group with which it is associatedvia line 814, the more specific location within that group so identifiedis determined with the concept of SGLSVs herein illustrated at 815 andagain, determined similar to the procedure described in FIG. 1, supra,wherefrom is outputted via line 816 the location of the leak. The restof the procedures for identifying the leak location follows theprocedures given above for embodiment two. As explained in greaterdetail, infra, there are preferably provided one (not shown) firstsubgroup inference engine corresponding to each respective first groupinference engine with at least one input thereto of a correspondingSGLSV. Since second group inference engine 813 identifies just whichgroup the leak event is occurring in, thereafter only that particularfirst subgroup inference engine need be utilized to more specificallylocate its position in the boiler.

47. In FIG. 9, there is illustrated the architecture used in FIG. 8, butin much greater detail than is shown in said FIG. 8.

DESCRIPTION OF THE PREFERRED EMBODIMENTS IN COMBINATION WITH A DETAILEDDESCRIPTION OF THE DRAWINGS, SANS FIGS. 4 AND 5

48. For the sake of clarity and a better understanding of theapplicability of the illustrations of the various drawings, a moredetailed description of same is given below in combination with theteachings of the herein preferred embodiments of the instant invention.

49. Identification of Boiler Tube Leak Sensitive Variables.

50. Industrial boilers are normally provided with attendantinstrumentation designed to measure the so-called process variables.These process variables include, among other parameters, combustionairflow, steam pressures and temperatures at different points of theboiler, etc. Measurement of these process variables are necessary forpurposes of boiler control and monitoring. When a leak takes place in aboiler tube(s) some of these variables exhibit significant changes inresponse to the leak event. The first step, or stage, in the practice ofthe instant invention is to identify these process variables, which forthe sake of convenience, are herein termed leak sensitive variables(LSV). The “gist” underlying the principle of this approach comprisesassuming no a priori knowledge about the identification orcharacteristics of these sensitive variables. Accordingly, this approachis initially data driven. Such direction, of course, is necessary toallow for automatic design of the first phase or stage of the instant,new and novel detection system wherein no expert human input is requiredto perform such identification of these LSVs. However, once the LSVs areidentified, interpretation, including expert human input is utilized tocorrelate, compare, and identify the final list of LSVs.

51. Accordingly, the first step in the first stage of the instantidentification process was to select criterion required to quantify thesensitivity of a given process variable to the occurrence of a leakevent. The relationships herein initiated to so quantify the sensitivityof a given process variable is herein termed the sensitivity function(S(ν_(i))) as herein given below.

S(ν_(i))=abs(Δν_(i) /Δl)

52. Where Δν_(i) represent the change in the process variable inresponse to a change of in the occurrence and/or magnitude of a tubeleak Δl and abs denotes the absolute value thereof. The reason for usingsuch absolute value is due to the fact that, in response the occurrenceof a leak event, some LSVs such as the combustion airflow exhibit anincrease in value (in this case Δν_(i) is positive), while on the otherhand, the ID fan suction pressure exhibits a decrease in value (in thiscase Δν_(i) is negative). The sign of the change is irrelevant forperformance of the instant sensitivity analysis. Note that othersensitivity functions may be used as, for example, the relative changeof the process variable:

S(ν_(i))=abs(Δ_(ν) _(i) /ν_(i))

53. Given the data which has herein been recorded for the boilervariables and the corresponding leak level and location, the sensitivitycomputation is performed in the order of the steps illustrated in thelogic flow chart of FIG. 1.

54. Referring now more specifically to FIG. 1, the process variablesΔν_(i), i=1,2 . . . , m are input via 101 into data acquisition system102 or first storage block wherefrom each is moved via line 103 tosensitivity evaluation block 104, wherein the sensitivity of each leakprocess variable is computed. The resulting value of the sensitivityfunction for each leak process variable is then moved vial line 105 tocomparison block 106 wherein it is compared to a threshold T. The valueof T is determined using statistical analysis, the results of which aremodified by incorporation of the instrumentation accuracy factorwherein, because of certain inherent errors resulting from measurement,a given LSV may exhibit a certain degree of fluctuation, calledmeasurement error. The set of LSVs which do not pass the threshold testare eliminated via line 107, whereas those that pass this threshold testare transferred via line 108 to second storage block 109. Once the firstset of LSVs is obtained, the next task is to eliminate those of thesesaved LSVs which are redundant. This elimination test is accomplished inredundancy analysis block 111 upon LSVs being transferred thereto vialine 110. If two variables are correlated in redundancy analysis block111, as being dependent on each other, it may be deduced that theycontain similar information about the leak; and, accordingly one of themmust be eliminated as, for instance, via line 112. This eliminationprocess is necessary in order to limit the number of variables whichmust be used by the instant, new and novel detection system. Forexample, if the pressure variation at one end of a tube is known, onecan determine the pressure variation at another end of the tube.Therefore, only one pressure is required in order to ascertain thepressure in both ends. The decision making logic employed in redundancyanalysis block 111 as to whether or not two LSVs are dependent upon eachother looks at the response of the two variables to the same leak. Ifthe response of one variable can be deducted from the second, only oneof those variables is kept. Otherwise, the two variables are notdependent and both should be kept. It will now be appreciated that up tothis stage of program logic movement, in the identification of boilertube LSVs, all the decisions reached were data driven. No human judgmentinput was used in the processing of any of the previous steps.Subsequently, the independent sensitive variables which are determinednot to be redundant are moved from redundancy analysis block 111 vialine 113 to physical interpretation block 114 where they are thereaftertested using expert human input or the principles of thermodynamics andmechanics to determine if they are physically dependent of tube leaks.The principal object of this operation is to ensure that each LSV in theresulting final list can be shown to be physically correlated to aboiler leak event. For example, when a tube leaks, its pressure must godown. Thereafter, those LSVs which do not pass the physical validationtest are eliminated via line 115, whereas those which pass such physicalvalidation test are transferred via line 116 to third storage block 117to be used later for tube leak detection. It is worth noting that thelast test is only needed to increase the confidence level in theinstant, new and novel identification scheme. For instance, if enoughdata is available to repeat the test many times and if the sensor datais of reasonable quality, physical interpretation block 114 would not berequired. The final list in third storage block 117 constitutes the setof LSVs and represents the information source used by the instant tubeleak detection systems which use will be discussed in greater detail,infra. As noted supra, although not shown in FIG. 1, it will be readilyappreciated by those skilled in the art that an additional informationcollection means or storage block may conveniently be disposed betweenredundancy block 111 and interpretation block 114, i.e., in line 113 sothat the sensitive variables which pass the redundancy test may becollected and more conveniently later validated in the operation ofinterpretation box 114 by human input thereto for making the necessarycomparisons with such changes in those variables with the standard ofprinciples of thermodynamics and mechanics.

55. It should be appreciated that this list of LSVs in third storageblock 117 may contain not only the ULSVs, but also the LLSVs, the groupLLSVs and/or the subgroup LLSVs. These four classes of sensitivevariables will also be discussed in greater detail later. Given all theavailable boiler process variables, the LSVs will be recorded whenever aleak takes place. Such recorded data, called training data, will be usedto train the ANNs used herein for leak detection. It is also worthnoting that, contrary to acoustic based detection systems, the instant,new and novel method, technique, and system(s) does not require any newinstrumentation to be added to an operating boiler since it uses onlyalready monitored process variables.

56. Design Philosophy.

57. When a boiler tube starts to leak, the sensitive variables start tochange in response to that leak event. For example, there has beenobserved both an increase in draft loss and steam temperature inresponse to a leak. It is also worthy of note that such variables mayalso change as a result of other phenomenon such as changes in boilerloading due to changing requirements for steam output. The significanceof change due to such other factors is referred to in greater detail,infra. From the perspective of information collection or storage thevariation of the LSVs in response to a leak event is the principalsource of information upon which the instant invention and inparticular, the first embodiment thereof makes a decision about whetheror not there is a leak event, and if so, where in the boiler the leak islocated. At the outset of making the instant invention, the challengewas how to recognize a leak event based upon information relating to thechanges of those variables. It was discovered that in order to find asolution to this problem, the correlation between just how a tube leakin a given location affects the so-called sensitive variables must bedetermined. Therefore, the principal consideration required foreffecting an effective solution to the instant tube leak detectionproblem is to first build, create, or otherwise assemble and thereafterlearn the map interlinking the interdependent parameters comprising theappropriate LSVs, the presence of a leak, and the location of that leak.

58. To assemble or provide and thereafter learn such a map, severaltools are available. For instance, classical identification techniquescould be used. However, this tool is based upon a model-based approachand requires a priori knowledge of the form of the map between the leakand the sensitive variables. Given the likely complexity of this map,providing the correct functional form a priori may not be a trivialtask. The second tool employs the use of model-free estimators to learnthe map. Here ANNs come in handy. The ANN, once appropriately trainedwill contain the desired map between LSVs, the leak size, and the leaklocation. ANNs operate on data and learn by examples. Its logic is theso-called crisp logic and the map learned by the ANN will be a crispmap. Since the sensitive variables are measured with sensors ortransducers whose outputs are not technically perfect, such measurementinformation or output is expected to be cluttered with backgroundacoustical noise. Accordingly, an amount of preprocessing of such sensoroutput information is required before the measurements of the sensitivevariables can be effectively used by the instant ANN based detectionsystem.

59. The second embodiment of the instant invention employs a somewhatmodified design philosophy from that described above for the instantfirst embodiment. This second embodiment is designed for use, forexample, on older style fossil fuel plant boilers which were designed tooperate on considerably less monitoring and instrumentation. The systemsand methods employed in the practice of the third and fourth embodimentsof the invention are able to operate on noisy data and incompleteinformation through the utilization of approximate reasoning.Approximate reasoning as described and used herein employs fuzzy logic.Approximate reasoning requires a knowledge base. To acquire suchknowledge boiler data is used to learn the so-called fuzzy map betweenthe LSVs, the leak level, and the location of a leak. The fuzzy modelingconsists of a set of “If Then” rules which use fuzzy sets. Tocharacterize these fuzzy sets, an ANN is used in connection with plantdata to identify the parameters of these fuzzy sets. More detailedinformation about the identification of these tube may be found in T.Zhong, A. T. Alouani, and R. A. Smoak, “On The Identification OfSensitive Variables Of Boiler Tube Leaks,” Proc. 29th IEEE SoutheasternSymposium on Systems Theory, Cookeville, Tenn., March 1997.

60. Embodiment One of the Instant Invention Comprising an ANN Based TubeLeak Detection System. In this first design of the instant, new andnovel boiler tube leak detection system, the problem of early tube leakdetection is approached and solved in two steps. In the first step, thedetermination is made as to whether or not there is a leak eventanywhere in the boiler. Once a leak event is confirmed, the second steppracticed is to determine its location.

61. In order to determine whether a leak is present in the boiler, theconcept of universal leak sensitive variables (ULSV) is introduced.ULSVs are process variables which respond to most leak events whichoccur in the several different locations of a boiler. The list of ULSVsis given in Table 2 below. TABLE 2 List of Universal Leak SensitiveVariables VARIABLES MEANING WDIWS COLDWELLTANK MAKEUP FLOW FFD(2)COMBUSTION AIR FLOW A1 PPRX(2) ID FAN A INLET SUCTION PRESS

62. Referring now more specifically to FIG. 2, it may be appreciatedthat the ULSVs will be used as input to an ANN based detector, called atube ULDS and identified at block 211. The output of ULDS block 211 iszero when no leak is present in the boiler and one whenever a leak ispresent. More detailed information relating to such ULDS is found in T.Zhong, A. T. Alouani, and R. A. Smoak, “ANN Based Tube Leak DetectionSystem,” Proc. 29th IEEE Southeastern Symposium on Systems Theory,Cookeville, Tenn., March 1997. As noted above, once a leak event isdeclared, the second step for practice of the instant invention is todetermine its location in the boiler. For this reason the concept oflocal leak sensitive variable (LLSV) has been introduced into theteachings, practice, and operation of the instant, new and novelinvention. A LLSV is a variable whose response to a leak in a givenlocation is much higher than in any other location. For each and everylocation in a boiler which has been determined and identified in thepractice of the instant invention to be a likely location where a tubeleak may take place, the procedure taught in FIG. 1, supra, is used toidentify independent LLSVs. Then for every such location an ANN baseddetector, herein termed, for convenience, a LLDS, is designed to detectthe leak event whenever it occurs in that particular location. Referringagain to FIG. 2, the input to each of the LLDS shown generally at theeleven blocks comprising 213, i.e. from LLDS 1 through LLDS 11, is thecorresponding set of relevant LLSVs (i.e. LLSV 1 through LLSV 11,respectively). The amplitude of output of each of the LLDSs is the leaklevel, wherein a level of zero indicates no leak is present in thatlocation. Once ULDS block 211 declares the occurrence of a leak event,all local leak detector systems, LLDS 1 through LLDS 11, comprising theblocks at 212 initiate a search for its most likely location. It will beappreciated by those skilled in this art that the architecture of theinstant, new, and novel system provides that, after ULDS block 211declares a leak event, each of the plurality of LLDSs, from LLDS 1 toLLDS 11 operate simultaneously in parallel, one to the other.

63. All of the ANNs used in said first embodiment have one input layer,two hidden layers, and one output layer. The input layer convenientlyhas three neurons, the first hidden layer has forty neurons, the secondhidden layer has twenty-four neurons, and the output layer has oneneuron. All such ANNs use the back propagation algorithm for training.

64. For a better understanding of just how the instant, new and novelANN based tube leak detection system operates, consider a procedurewherein data recorded for a given leak event included information of theULSVs in Table 2, supra, which was usually taken for a period of timedetermined to be convenient and reliable, herein preferably some 15days, to train the ANN comprising ULDS block 211. It is noted that thisis, and was accomplished as follows. Given the variation of the tubeleak over a time interval, Δt (typically 5 min), the variation of theULSV, Δν_(i), i=1, 2 ,3, is computed from the 15 days of recorded data,supra. The process is repeated until all the data is used. At the end ofthis process, all the Δν_(i), i=1, 2, 3, and the corresponding Δl arestored in an input array and an output array, respectively. Such arraysconstitute the training data for the ANN comprising ULDS block 211. TheANN is then trained with this data until the learning error diminishesand stays below a threshold of 0.0001. Once the training is completed,the weights and biases obtained are stored and later used for detection.

65. Referring now more specifically to FIG. 3, the input U of the ANN isgiven by:

U=[Δν ₁ Δν₂ Δν₃]

Δν_(i)=ν_(i)(t ₁)−(t ₂)

t ₁ −t ₂=5 mins

66. The output Y of the ANN is the leak variation due to the input U.The signal Yd is the target leak variation, i.e., for a given U there isa corresponding Yd which was obtained from actual operation of theboiler or, alternatively, it may be obtained as simulation data. Thetraining consists of adjusting the weights (w1, w2, and w3) and biases(b1, b2, and b3) of the ANN until Y matches Yd. As long as Yd is notequal to Y, an error signal e=Yd−Y is formed and used to adjust theparameters (weights and biases) of the ANN. In FIG. 3, the block F1, F2,and F3 are sigmoids of the ANN. Again, and for example, referring to theparticular operation of ULDS block 211 in FIG. 2, the trainingparameters used for ANNs of the type illustrated in FIG. 3 are givenbelow in Tables 3-6, it being understood that the matrices and vectorsshown are illustrative and are not intended to be limiting of similarmatrices which conveniently may be utilized for the instant invention toadjust weights and biases of other like ANNs. Note that Table 3represents a 40×3 matrix, i.e. three input variables, one each to thethree neurons in the input layer, which three neurons are in turnconnected with the forty neurons in the first hidden layer. Table 4represents a 40×24 matrix, i.e., forty neurons in the first hidden layerconnected with twenty-four neurons in the second hidden layer. Table 5represents a 24×1 matrix, or more correctly, a 24×1 vector, i.e.,twenty-four neurons in the second hidden layer in weights of connectionbetween same and the one neuron in the output layer. Table 6 representsthe bias values of the neurons used by the ANN. TABLE 3 Tube UniversalLeak Detection System Weight W1 Matrix −6.8105 0.1830 0.0084 0.3421−0.0835 −1.4036 0.4772 0.0646 6.3625 0.6104 −0.0061 −1.6130 −0.1262−0.0562 11.8071 0.1153 0.0877 −9.9972 −0.0612 0.0186 −9.5827 0.1654−0.0499 −1.5191 0.2763 −0.0829 −9.9653 −0.4789 −0.0210 −7.8367 0.17220.1217 −0.0732 0.1993 0.0194 4.8121 −0.2215 0.0263 −6.0527 0.3517 0.09926.6523 0.2443 −0.0891 6.1700 0.4451 −0.1021 0.4342 −0.3198 0.0673−7.3625 0.2749 −0.0777 2.7059 −0.2872 −0.0236 −6.5131 −0.3797 0.09532.0727 0.1964 0.0710 −3.2275 −0.1660 −0.0673 −9.0968 0.2178 −0.05286.7786 −0.7184 0.0301 2.8051 −0.4700 0.0904 0.3274 −0.0161 −0.09588.5316 0.3651 0.0325 8.7681 −0.1717 −0.0825 5.8704 −0.4336 0.0087 4.6249−0.1582 0.0887 −0.5091 −0.4896 0.0638 −8.5882 0.1230 0.0652 6.26180.3068 0.0029 −8.6258 0.3348 −0.0832 −8.5491 0.2013 0.0946 −8.0004−0.1078 0.0576 −7.8817 0.3413 −0.0113 0.6119 −0.3323 0.1151 −6.0921−0.0459 0.0019 −3.3405 0.1767 −0.0253 −7.5726

67. TABLE 4 Tube Universal Leak Detection System Weight W2 Matrix 0.07380.0071 0.1802 −0.1672 0.0116 0.0198 −0.0789 0.0287 0.0547 −0.1131 0.13850.1301 0.1384 −0.0142 −0.1465 0.0054 0.1891 −0.1117 −0.1385 0.05930.0522 0.0072 −0.1133 0.1827 −0.0055 0.2204 0.0493 −0.0898 −0.05100.2296 −0.10323 0.1120 −0.1451 0.1744 −0.0010 0.0567 0.0986 0.10060.1626 0.0402 0.0507 0.0166 −0.1419 −0.0829 −0.1836 −0.1017 −0.00880.1494 0.0819 0.1841 −0.0815 0.1144 0.1560 0.1276 0.1650 −0.1363 −0.1751−0.0039 −0.0593 0.0602 0.0362 −0.1723 0.0693 −0.0236 0.1443 −0.15020.0543 0.1259 0.0202 −0.0445 0.0420 0.1565 −0.0063 0.0855 −0.1441 0.10460.1036 −0.0805 −0.1871 0.0375 0.0938 0.1462 0.0330 0.1403 −0.0841−0.0865 0.0325 −0.1915 0.0795 −0.1255 0.1851 −0.1689 −0.1840 0.0732−0.2028 0.0170 0.0181 −0.2342 0.0981 0.0246 −0.1878 −0.1935 −0.2195−0.1745 0.2297 −0.0976 0.1063 −0.1310 −0.1609 −0.1503 −0.0396 −0.10400.2043 0.0374 0.0680 0.1182 −0.0528 0.1904 0.0487 −0.1855 −0.1864−0.1240 −0.0365 0.1475 −0.0594 −0.1358 0.0552 0.0802 0.0072 0.1511−0.1138 −0.0829 0.1787 −0.2018 0.0722 0.1055 −0.0715 −0.0116 −0.12290.1131 0.1461 −0.0653 −0.1448 0.0348 −0.0440 0.0522 0.0692 0.0276−0.2240 −0.1107 0.0475 0.1777 −0.0979 0.0389 −0.0291 −0.0735 0.0815−0.0905 −0.0918 0.1564 −0.0398 0.1149 −0.0744 0.0080 0.1720 −0.0784−0.1704 0.0048 0.0256 0.1939 0.1185 −0.0360 0.0969 0.0185 0.0541 −0.1209−0.0401 −0.1085 −0.1493 0.2022 0.1810 −0.2025 −0.0870 0.0466 −0.05250.1803 −0.0790 0.0786 0.0603 0.2035 0.1847 −0.1434 0.1012 0.0249 0.1391−0.1038 0.0587 0.1706 −0.1037 −0.1067 −0.1147 0.1792 −0.1084 −0.03790.0514 −0.1542 0.0001 0.0923 −0.1863 0.1070 0.0895 0.0258 0.0892 −0.17910.1140 −0.0848 0.1233 0.1344 −0.1617 −0.1451 −0.1280 0.1280 0.0885−0.2216 0.1954 0.1661 −0.0599 0.0210 −0.1129 −0.0155 0.0856 −0.00810.0448 0.1396 −0.0116 0.0479 0.0782 −0.1142 −0.1152 −0.1823 −0.17470.0500 0.0406 0.1218 0.1815 0.1533 −0.1227 0.0033 −0.0088 −0.2110−0.1825 −0.1559 0.1346 −0.0885 0.1847 0.1589 −0.1215 0.1386 0.09760.0545 −0.0985 −0.0830 −0.0276 −0.1225 0.1726 −0.0255 −0.0201 0.0482−0.0302 −0.1679 0.0856 0.0231 −0.0502 −0.2117 −0.0630 −0.1635 0.0963−0.1471 −0.0635 −0.1792 −0.1205 −0.1977 −0.1612 −0.0475 −0.0826 0.0595−0.1119 0.1025 0.0493 −0.1749 −0.0554 −0.1992 0.0432 −0.1617 0.0003−0.2067 −0.0815 0.0239 −0.0573 −0.0292 −0.0211 0.1185 −0.1522 0.0271−0.1341 0.1224 −0.0373 0.1868 0.1723 −0.1436 −0.0161 0.1408 0.1930−0.1730 −0.1506 −0.0841 −0.0581 −0.1197 −0.0556 0.1410 0.0157 −0.09160.0922 0.1557 0.1459 0.0347 0.0211 −0.1832 0.1204 0.0129 −0.0922 −0.11200.1248 0.1169 −0.1186 −0.0569 −0.1377 −0.0457 0.1321 −0.1221 0.0420−0.1806 0.1842 −0.1891 0.1053 0.0608 −0.0229 0.1524 0.1764 0.1412−0.1272 −0.1600 0.0023 0.0734 −0.1813 0.0180 0.0491 −0.1567 0.18060.1365 0.1773 −0.0209 0.1603 0.0586 −0.1291 0.0701 0.0345 −0.1747−0.1115 0.0143 0.1254 0.0596 −0.1416 0.1789 −0.1310 −0.0654 −0.04550.0133 0.0794 0.0220 0.0545 −0.0560 −0.1071 0.1274 −0.1589 0.0589−0.0177 −0.1684 0.0016 −0.0498 0.1558 0.0112 0.1828 0.1851 0.1820−0.2024 0.0326 0.1823 0.0621 −0.0206 0.0621 −0.0767 −0.2037 0.0471−0.1237 0.1416 0.1274 −0.1289 0.1493 −0.2024 −0.1794 0.1712 0.16220.0217 0.1168 0.0818 0.1873 −0.0464 0.1054 −0.1196 −0.0819 −0.0198−0.1253 0.0215 −0.1765 −0.0312 −0.1623 −0.0562 0.0723 −0.1289 0.1578−0.0472 0.0443 0.0789 0.1851 −0.0916 0.1273 −0.0624 −0.1304 0.00820.1423 −0.0210 −0.1409 −0.1069 −0.0866 0.1241 −0.1653 0.0954 −0.19180.0656 0.1694 −0.0259 0.1013 −0.0922 −0.1518 −0.1902 −0.0580 0.12380.0044 −0.1028 0.0306 0.1199 −0.1135 −0.1772 0.1802 −0.0632 0.0611−0.1816 −0.0705 0.1752 −0.1555 −0.1427 0.0054 0.1931 −0.1598 −0.1749−0.0439 −0.0538 0.0318 0.0718 0.1082 0.1323 −0.0502 0.0083 −0.0877−0.1700 0.1609 −0.0907 −0.1661 −0.1016 0.1517 0.0428 −0.0182 0.11430.1947 −0.0936 0.1457 −0.0081 −0.0652 0.0792 0.0039 0.0692 −0.1529−0.0917 0.0657 0.1612 0.1575 0.1464 −0.0996 0.1779 −0.0345 −0.16880.1163 0.0640 −0.0157 0.1987 −0.0563 −0.0832 0.1566 0.0905 −0.0124−0.0911 0.0994 0.0825 −0.0810 0.0724 0.2001 0.1737 −0.1849 −0.0060−0.1523 −0.1090 0.1375 0.0061 −0.0786 0.1978 0.0704 −0.0129 −0.1424−0.1607 0.1591 0.1309 0.1480 0.0999 0.0438 0.1884 −0.0660 0.1053 0.0182−0.1339 0.1168 0.0861 −0.0038 −0.1265 0.0822 −0.1164 0.1430 0.1837−0.1324 0.1622 −0.1086 −0.1788 −0.1344 0.0045 −0.1839 −0.0327 0.1944−0.1103 0.1657 −0.0477 −0.1037 0.0770 0.0808 0.0057  −01916 0.09030.0294 −0.1813 0.0440 0.0673 −0.2375 0.1845 0.1964 0.1569 −0.1933 0.14560.2119 0.1723 −0.0993 0.1792 0.1805 −0.1861 −0.0035 0.0796 0.0307−0.1924 −0.0219 0.1789 0.1010 0.1826 −0.0442 0.0763 0.0191 −0.19520.1524 0.0942 0.2080 0.1789 0.0436 0.0001 −0.0013 −0.0201 −0.0121 0.04080.1154 0.0765 −0.1875 −0.1062 0.0771 −0.1768 −0.1274 −0.0247 0.0345−0.1175 0.0511 0.1165 0.0028 −0.0611 −0.0355 −0.0295 0.0818 −0.1670−0.1336 −0.0003 0.1071 −0.1695 0.2059 −0.0811 0.0364 0.1256 −0.0259−0.0860 −0.1484 0.1955 −0.1766 −0.1578 0.0406 −0.0111 −0.1744 0.13840.2334 −0.1805 0.1340 0.1251 −0.0845 0.0210 0.1689 0.0759 0.1521 −0.08310.0816 −0.0637 0.0961 −0.0567 −0.1975 0.0731 −0.1487 −0.0211 0.17040.0994 0.0648 0.0861 −0.1515 −0.0252 −0.0581 0.1628 0.1709 0.1075−0.0661 −0.0584 −0.1650 −0.0660 0.1852 −0.1762 0.0361 0.1796 −0.19160.1560 0.0367 −0.0528 −0.0254 0.1581 −0.0263 0.1743 0.0171 −0.11820.0768 −0.0623 0.1661 0.0852 0.1346 0.0998 −0.0005 −0.0130 0.0397 0.15490.1150 −0.1167 −0.1453 −0.1609 −0.1245 0.0824 0.0403 0.1285 −0.14650.1910 0.1468 −0.0390 −0.0369 0.1561 −0.0579 −0.1816 0.0079 −0.0485−0.0651 −0.0146 −0.0487 −0.1712 0.1360 0.1807 0.1588 −0.0006 −0.19920.0195 −0.1760 0.0410 0.0823 −0.1678 −0.1126 −0.1595 −0.0424 −0.0392−0.0280 0.2234 −0.1263 0.1079 0.0244 −0.1185 0.1310 −0.1313 −0.1180−0.1625 0.1580 0.0207 −0.0402 −0.1761 0.0075 −0.1427 0.1637 0.0940−0.1152 0.1368 −0.2040 −0.0220 −0.1628 0.0081 0.0833 −0.1096 0.07770.0392 0.2297 0.0518 −0.1907 0.1610 0.0464 −0.1397 0.0995 −0.0207 0.0363−0.1095 0.1070 −0.0596 0.1297 −0.1587 0.0379 −0.0389 −0.0591 0.1764−0.1232 −0.0003 0.0469 −0.0557 −0.1023 0.0922 −0.1084 −0.0826 −0.07850.1489 0.1754 −0.0628 −0.0048 −0.1488 −0.0232 −0.0027 −0.1915 −0.13080.1180 −0.0712 −0.1716 0.1256 0.1255 −0.1634 −0.1678 0.1470 0.15520.0937 −0.1024 0.0389 −0.1066 0.0481 0.1905 0.1352 −0.1915 0.1182−0.0551 −0.1976 −0.0637 0.0316 0.1230 0.0138 0.0530 −0.0158 −0.05590.0474 −0.0524 0.1226 0.1329 0.0558 0.1673 0.1132 0.0343 −0.0487 0.1755−0.1950 −0.0207 0.1685 −0.0581 0.1379 0.0771 0.0743 −0.1601 −0.14560.0579 0.1570 0.1210 0.0074 −0.1279 −0.1511 −0.1509 −0.1640 −0.0533−0.1859 −0.1240 0.0624 −0.1856 −0.1878 −0.0632 0.0473 0.0168 0.1460−0.0043 0.0443 −0.1710 0.0542 0.0222 0.0550 −0.0106 −0.1163 0.0221−0.1636 −0.2199 0.0806 −0.1386 0.0068 0.1188 −0.1033 0.1807 0.17720.1686 −0.1032 −0.0171 0.1407 −0.0189 −0.1568 0.0917 0.0649 0.08940.0423 0.0440 0.1741 0.1676 −0.1578 0.0060 0.1379 0.0536 −0.0461 0.04860.1231 0.1647 −0.1207 0.0659 −0.1292 0.0930 0.1197 −0.1299 0.1541−0.0726 −0.1206 −0.0084 0.1790 0.0529 0.1027 −0.1761 0.1499 0.13840.0713 0.0234 0.1460 0.0772 0.1033 −0.0678 −0.1148 0.0128 0.0688 −0.1113−0.0149 −0.0549 0.1460 0.1697 0.0531 0.1058 0.0748 0.0082 0.2197 0.0596−0.0919 0.2260 −0.0124 0.1763 0.1825 0.0473 0.1479 0.0732 0.0032 0.1452−0.1110 −0.0373 0.1456 −0.1043 −0.0833

68. TABLE 5 Tube Universal Leak Detection System Weight W3 Vector−0.9877 0.54 −0.9356 −0.353 0.6271 0.8469 −0.9765 −0.0795 −0.6412 0.90320.7329 −0.336 0.0923 −0.3751 −0.4593 0.6697 0.8555 −0.1294 −0.2055−0.2035 −0.0055 −0.0649 −0.1163

69. TABLE 6 Tube Universal Leak Detection System Biases b1 b2 b3 −0.9647−0.5533 −0.6762 0.822 0.3251 −0.8476 0.5472 0.407 0.3461 0.5322 −0.4323−1.9469 0.2148 1.6961 0.3997 0.9917 0.1978 0.3949 0.7243 −2.3931 −0.248−0.9871 −0.5398 1.5815 −0.0803 −3.846 0.2239 −0.3644 −0.1711 3.1565−0.3525 2.2384 −0.1407 −2.4713 −0.237 1.4708 0.5548 0.467 0.2226 −0.38620.7471 −2.5361 0.1897 1.0673 0.5709 1.9486 −0.2815 −2.4204 −0.7172−1.2028 0.013 1.4461 3.3807 −2.8041 −3.8789 −3.2556 0.9405 0.8649 1.2816−3.2461 −1.3507 1.2918 −1.1333 0.7896 0.9118

70. For determination of the location of tube leaks in one of manyboilers which are operated by the Tennessee Valley Authority (TVA) as,for example, Kingston 9 Boiler, leaks in eleven different locations canbe detected by this using the LLDSs, LLDS 1 through LLDS 11, shown inFIG. 2, supra. These different locations are:

71. Location 1: Superheater Front Radiant Platen (SHFRP)

72. Location 2: Reheater Front Radiant Platen (RHFRP)

73. Location 3: Superheater Waterwall (SHWW)

74. Location 4: Superheater Intermediate and Pendants (SHIP)

75. Location 5: Superheater Outlet Pendants (SHOP)

76. Location 6: Reheater Intermediate and Pendants (RHIP)

77. Location 7: Reheater Outlet Pendants( RHOP)

78. Location 8: Reheater Waterwall (RHWW)

79. Location 9: Primary Superheater (PSH)

80. Location 10: Superheater Economizer (SHEC)

81. Location 11: Reheater Economizer (RHEC)

82. The local sensitive variables for each location are given in Table7, infra. TABLE 7 Arrangement of Local Leak Sensitive Variables LLSV 1LLSV 2 LLSV 3 LLSV 4 LLSV 5 LLSV 6 WFH5TOT LFNTLTR (1) LCLW ncfdcs (1)WFH5TOT WFH5TOT PDA PFNGRM PWBS PFNGSM PFNGRM PIPIV ncfdcs (1) PWBSWDIWS PBPGX (11) PWBS LFNTLTR (1) LLSV 7 LLSV 8 LLSV 9 LLSV 10 LLSV 11WFH5TOT TBPSI (13) TBPSX (20) LFNTLTS (1) PBPGX (7) PIPIV PHPFSFOX (1)PFNGRM PFNGSM FGSDAI LFNTLTR (1) PBD TBPSX (20) PBPGX (14) TMSDCS

83. Referring again and back to FIG. 2, each of the LLDSs showngenerally at 213, outputs its decision about the presence of a leak toinference block 214. The final determination of the location of the tubeleak is made at inference block 214. The decision in block 214 is madeas follows. Each of the eleven LLDS blocks use the corresponding LLSVinput and estimates the leak as if it were present at that location. Theoutput of each LLDS is sent to inference block 214, wherein is made suchfinal determination by choosing the location whose LLDS has the largestleak signal. To illustrate in greater detail just how this works, thereader's attention is directed to Example I, discussed infra in theExamples section.

84. In the first embodiment of the instant invention, eleven likelyboiler locations can be detected by the instant, new and novel ANN basedleak detection system. The number of process sensitive variablesrequired by the system is twenty-three (three ULSVs and twenty LLSVs).The list of these variables is shown in Table 8, below. TABLE 8 List ofLocal Leak Sensitive Variables required by the First ANN Based Tube LeakDetection System VARIABLES MEANING FGSDAI LEAK-OFF HDR FLOW LCLWCOLDWELL TANK LEVEL LFNTLTR (1) RH BR TILT CNR #1 POSITION LFNTLTS (1)SH BR TILT CNR #1 POSITION ncfdcs (1) Pulv A Feeder Flow PBD BOILER DRUMPRESS-NORTH PBPGX (11) SH FURNACE PRESS AFTER HT SUPHT PBPGX (14) SHFURNACE PRESS AFTER ECONOMIZER PBPGX (7) RH FURNACE PRESS AFTERECONOMIZER PDA DEAERATOR PRESSURE PFNGRM RH FURNACE DRAFT A PFNGSM SHFURNACCE DRAFT A PHPFSFOX (1) TURB FIRST STAGE PRESS A PIPIV HOT REHEATSTEAM INT VLV A PRESS PWBS SH FURN WINDBOX PRESSURE TBPSI (13) RHATTEMPT A BEFORE SPRAY STM TEMP TBPSX (20) RH OUTLET HEADER TEMPERATUREA TMSDCS SH SEC OUTLET HDR TEMPERATURE A WDIWS COLDWELL TANK MAKEUP FLOWWFH5TOT CONDENSATE FLOW TO DEAERATOR

85. This design is quite suitable for a modern boiler, wherein a largenumber of process variables is monitored and wherein the instantembodiment one of the new leak detection system will not requireadditional instrumentation. For effecting early tube leak detection inolder boilers, this may not be the case since not all the abovevariables are being monitored by the instrumentation. For this reason,another design, embodiment two of the instant invention, has beendeveloped and is taught below which requires the input of a smallernumber of process variables as such smaller number are being monitoredby boilers of older designs.

86. Note: More specific reference to FIG. 4 and FIG. 5 is made below inconjunction with the discussions of Examples I and II, respectively.

87. Embodiment Two of the Instant ANN Based Boiler Tube Leak DetectionSystem.

88. Referring now more specifically to FIG. 6, it will be appreciatedthat in the practice of this second embodiment, the likely locations atwhich a tube leak may be, or is likely to occur has conveniently andexpeditiously been divided into four groups (four subsystems of theboiler in this case): the economizer (EC), the waterwall (WW), thesuperheater (SH), and the reheater (RH). In the practice of embodimenttwo, the presence of a leak in any one of these groups is monitored. Inorder to detect a leak event in a group of location, the concept ofGLSVs is introduced. These are variables which exhibit significantchanges whenever a leak event occurs in any one of the four systems(four groups).

89. The GLSVs, supra, are used as input to ANN based GLDS 612 comprisedof GLDS 1 through GLDS 4, whose output to inference engine block 613 isan indication of the leak level. Similarly to the LLDS 1 through LLDS 11in FIG. 2, supra all of these GLDSs comprising 617 work in parallel inorder to detect, at the earliest possible moment, the presence of a tubeleak. Again, just for example, an output therefrom at level zeroindicates no leak is present in that group location, it beingunderstood, of course, that, if desired, a level of one could be soutilized. The procedure of identification of the different variablesuses the logic of flow shown in FIG. 1, supra, with a comparison made incomparison block 106 against a threshold value predetermined andspecific to GLSVs. The arrangement of the GLSVs within each of the fourgroups for this second embodiment is given in the Table 9, below. TABLE9 Arrangement of Group Leak Sensitive Variables GLSV 1 GLSV 2 GLSV 3GLSV 4 PDA PBPGX (13) PPRX (2) PBPGX (13) PBPGX (6) CHWMUVO CHWMUVOCHWMUVO PBPGX (13) PFD (2) PBPGX (7) PFD (2) WFH5TOT XFNO2RFX (1) PBPGX(11) XFNO2RFX (1)

90. Referring again more specifically to FIG. 6, it may be appreciatedthat the output of each of GLDS 1 through GLDS 4 is sent to inferenceengine block 613, wherein is made the determination of the group inwhich the leak has taken place. After this second determination, thethird and next step is to determine where the leak event is locatedwithin that group. For this reason, the concept of SGLSVs is introduced.The number of subgroups depends on the availability of theinstrumentation and is boiler dependent. The final determination of justwhere the leak event is located in a particular group utilizing subgroupleak detection procedure and is effected in subgroup leak locationsystem, generally illustrated at 615.

91. More specifically, similar to the determination of the GLSVs in theprocedure taught above in conjunction with FIGS. 1 and 6, there is alsodetermined by employment of similar procedure, a set of SGLSVs, whereinstill a different threshold value is utilized therefore in the operationof comparison block 106 than was the threshold value employed duringdetermination of such GLSVs. In the most preferred arrangement, arelatively small number, usually an average of three, of said SGLSVs isassociated with each of said four boiler subsystems. To determine whichsubgroup contains the leak the SGLSVs of each subgroup are input to ANNspreviously trained for handling such input and wherefrom there is outputto a subgroup inference engine contained in 615 wherein is determinedthe particular location of the leak event.

92. It will be appreciated that the detection of leak events, in anolder design boiler by means of utilizing the four subsystems supra,requires only eleven process sensitive variables rather than thetwenty-three required for more modern boilers treated in accordance withembodiment one, supra. These eleven variables are shown in Table 10,below. TABLE 10 List of Group Leak Sensitive Variables Required by theSecond Embodiment of the Instant ANN Based Tube Leak Detection SystemVARIABLE MEANING PDA DEAERATOR PRESSURE PBPGX (6) RH FURNACE PRESSUREAFTER LT SHTR PBPGX (13) SH FURNACE PRESSURE AFTER LT SHT WFH5TOTCONDENSATE FLOW TO DEAERATOR CHWMUVO HOT WELL MAKEUP VALVE DEMAND FFD(2) COMBUSTION AIR FLOW A1 XFNO2RFX (1) RH FRNC OXYGEN ANALYZER PROBE APPRX (2) ID FAN INLET SUCTION PRESSURE PBPGX (7) RH FURN PRESSURE AFTERECONOMIZER PCRHT COLD REHEAT STEAM FROM TURB PRESSURE PBPGX (11) SHFURNACE PRESSURE AFTER HT SH

93. Neuro-Fuzzy Based Boiler Tube Leak Detection System ComprisingEmbodiments Three and Four.

94. Up to the present time, the ANN based leak detection system,described supra, has performed very well on Kingston 9 boiler.Additional testing of the tube leak detection system is under way usingother TVA boilers. In order to further improve the likelihood of earlydetection of boiler tube leaks, another design philosophy is nowproposed. Because the critical procedure in successful detection ofboiler tube leak events is to learn the map between tube LSVs and leaklevel and location, the ANN based tube leak detection system of eitherembodiment one or embodiment two, supra relies solely on numerical data.An alternate and new design comprising embodiments three and four of theinstant invention attempts to arrive at decisions more in the way thatthe human brain functions by combining ANNs with fuzzy logic. It usesnumerical data as well as linguistic information provided by experiencedhuman operators to learn such a map. The benefits of such still newerdesign for embodiments three and four are many. First it increases therobustness of the leak detection system in the presence of sensorinaccuracies and noisy environment. The new fuzzy logic system does notrequire accurate data. It operates on vague information and performsapproximate reasoning. Second it can operate on incomplete information.In case of sensor failure, the system can still make decision aboutleaks. Accordingly, two neuro-fuzzy leak detection systems are underdevelopment. The first utilizes the same sensitive process variablesinformation as the first embodiment which comprise an ANN baseddetection system operated in accordance with the procedures shown inFIG. 2 supra, except that herein the ANN based detectors are replaced byinference engines.

95. Referring now more specifically to FIG. 7, such system is shown andin particular such inference engines, comprised of ULDIE shown at 711and LDIE 1 through LDIE 11, shown generally at 713. To make inference,each of the LDIE, LDIE 1 through LDIE 11, use a knowledge base. Saidknowledge base comprises the fuzzy map between a leak in a givenlocation and the set of appropriate sensitive variables. This fuzzy mapis modeled by a set of “If Then” rules. An example of such a rule is:

96. R1: IF the change in Combustion Air is POSITIVE LARGE and the changein ID Fan Inlet Suction pressure is NEGATIVE LARGE THEN the leak isLARGE.

97. Note that the above rule involves linguistic statements, calledfuzzy sets, such as “Large.” Each one of these fuzzy sets will have acertain membership function. In this instant still newer designed systemall of the membership functions are of triangular shape. They arecharacterized by their center (c) and spread (s). The key issue here ishow to determine the center and spread of the membership function ofeach fuzzy set involved in the different rules without the need of anexpert person. To accomplish this, each LSV is represented by a fuzzyset. The universe of discourse of each LSV, ν₁, is divided into n_(i)partitions. The maximum number of rules N is equal to$N = {\prod\limits_{l = 1}^{m}\quad n_{l}}$

98. where m is the number of sensitive variables used as input to aparticular detection subsystem. Each one of these rules has the form ofrule R1 taught supra. To determine the parameters of the fuzzy setsinvolved in each rule, fuzzy artificial neural networks (FANN) andboiler leak data are used. The FANNs are required in order that theparameters of the knowledge base used by the different inference enginesmay be learned. The set of fuzzy rules for a given leak at a givenlocation constitutes a knowledge base of an appropriate inferenceengine. Such knowledge base contains the fuzzy map between the leak andits location and the set of corresponding process sensitive variables.

99. Referring now more specifically to FIGS. 7 and 8, two neuro-fuzzydesign strategies may be utilized in the practice of the instantinvention. The first, illustrated in FIG. 7 and comprising embodimentthree, uses the same process sensitive variables and processingarchitecture as used in the first ANN based tube leak detection systemtaught in embodiment one, supra. A universal leak detection block oruniversal leak detection inference engine (ULDIE) at 711, uses the ULSVsas input and produces a fuzzy output in the form of zero, small, medium,or large. Once a leak is confirmed, i.e. when (for this example) theoutput is either medium or large, all inference engines comprising LDIE1 through LDIE 11 and referenced generally at 713 begin, simultaneouslyand in parallel, to estimate the location of the leak. Their output issent in parallel to inference block 714 for final determination astaught supra.

100. More specifically, it will be appreciated by those skilled in thisart that in this arrangement there is utilized a tube ULDIE fordetermining the likelihood of an occurrence of a tube leak event as, forexample, in an industrial boiler. The ULDIE is operatively associatedwith inputs of observed changes of ULSVs, and comprises a first leakinference engine. The first inference engine is provided with both aknowledge base comprising a set of fuzzy rules describing the fuzzy mapbetween each of said ULSVs and the relative magnitude of said leakevent, and a database defining the membership functions utilized in saidfuzzy rules. It is also provided with a reasoning mechanism arranged forperforming inference procedures upon said set of fuzzy rules. Thearrangement is also provided with a plurality of LLDS, each of which isoperatively associated with inputs of one of a plurality of observedchanges in said industrial boiler of LLSVs each of said LLDSs comprisinga corresponding second leak inference engine. Each such second leakinference engine, in turn, is provided with both a knowledge basecomprising a set of fuzzy rules describing the fuzzy map between thecorresponding LLSV and the location of said leak event, as well as adatabase defining the membership functions utilized in said fuzzy rules.It is also provided with a reasoning mechanism arranged for performinginference procedures upon said fuzzy rules. The arrangement is alsoprovided with a third inference engine for receiving an output from eachof said plurality of LLDSs and for determining the location in theboiler of said leak event. The third inference engine is also providedwith a knowledge base comprising a set of fuzzy rules describing thefuzzy map between each of said LLDS and the location of said leak event,a database defining the membership functions utilized in said fuzzyrules, and reasoning mechanism arranged for performing inferenceprocedures upon said fuzzy rules, and of the output from each of saidplurality of LLDSs.

101. Still more specifically, operation of this arrangement for tubeleak detection, in its most preferred form, comprises determining forthe boiler, a set of tube ULSVs and representing each of said ULSVs witha fuzzy set comprising linguistic statements. It also comprises buildingboth a knowledge base for a respective first leak inference engine whichcontains a set of fuzzy rules describing the fuzzy map between each ofits corresponding ULSVs and the relative magnitude of said leak eventand a database for said corresponding first leak inference engine whichdefines the membership functions used in the fuzzy rules of saidknowledge base. The respective first leak inference engine furthercomprises reasoning mechanism for performing inference procedures uponsaid set of fuzzy rules for decision-making on the magnitude of saidleak event. In addition, there is determined for said boiler a set oftube LLSVs. Each of said LLSVs is represented with a fuzzy setcomprising linguistic statements. A knowledge base is built for each ofa set of second leak inference engines, each said second leak inferenceengine corresponding to one of said LLSVs, and each such knowledge basecomprising a set of fuzzy rules describing the fuzzy map between thatLLSV corresponding to that second leak inference engine and the locationof said leak event. For each of said second leak inference engines forwhich a knowledge base is built, there is built a database which definesthe membership functions used in the fuzzy rules with the knowledge basecorresponding to said second leak inference engine. Said second leakinference engines further comprises a reasoning mechanism for performinginference procedures upon its corresponding set of fuzzy rules fordecision-making on the location of said leak event. Thereafter saidindustrial boiler is monitored for the occurrence of a leak event byobserving changes in values in said boiler for each of said ULSVs andsupplying said observed changes in values to said first inference enginefor generating a fuzzy output therefrom. The resulting fuzzy output iscompared to a ranking of the linguistic statements. If the linguisticstatements are greater than a predetermined rank, it is concluded that aleak event is occurring. Thereafter changes in values from said boilerfor each of said LLSVs is observed and supplied to the LLSVscorresponding second inference engine for simultaneously producingtherefrom a fuzzy output. Each fuzzy output is simultaneously introducedto a third leak inference engine. The third leak inference engine isprovided with a knowledge base comprising a set of fuzzy rulesdescribing the fuzzy map between the location of said leak event andeach LLSV, a database defining membership functions used in the fuzzyrules of the third leak inference engine knowledge base, and reasoningmechanism for performing inference procedures upon each of said fuzzyrules for determining the location, in said boiler, of said leak event.

102. Referring now more specifically to FIG. 8, in such designcomprising embodiment four of the instant invention, all GLIEs,comprising GLIE 1 through GLIE 4 and shown generally at 812, use theirappropriate GLSVs, i.e. GLSV 1 through GLSV 4, respectively, as inputand their knowledge base to estimate a leak, if any. Their output issent to group inference block 813 for final determination of thelikelihood of the occurrence of a leak event and the particular groupwhich is “leaky.” Subsequently, only that inference engine representingthat particular group identified as leaky is inputted with subgroupfuzzy variables for purposes of leak location identification in block815. Further procedures are as previously discussed in the teachingrelating to embodiment two including the depiction in FIG. 1.

103. Referring now more specifically to FIG. 9, the architecture showngenerally in FIG. 8 is depicted for the arrangement herein shown ingreater detail. Although this architecture is somewhat similar to thatshown in FIG. 6, supra, inference engines are used instead of ANNs forboth the four groups and for the six subgroups with a separate inferenceengine utilizing the outputs of the four groups and another separateinference engine utilizing the outputs of the six subgroups, i.e., asecond GLIE and a second SGLIE, respectively. From the depiction, itwill be appreciated by those skilled in this art that therein areutilized a plurality of first tube GLDSs for determining the likelihoodof the occurrence of a boiler tube leak event wherein each of these GLDSis operatively associated with inputs of observed changes in saidindustrial boiler of at least one corresponding GLSV and comprises acorresponding first GLIE. Each first GLIE is provided with a knowledgebase comprising a set of fuzzy rules describing the fuzzy map betweensaid at least one corresponding GLSV and the relative magnitude andgroup location of said leak event, a database defining the membershipfunctions utilized in said fuzzy rules, and a reasoning mechanismarranged for performing inference procedures upon said set of fuzzyrules. A second GLIE receives an output from each of said plurality ofGLDSs and determines the likelihood of a leak event and thecorresponding GLDS in which such boiler leak event can be found. Thissecond GLIE is provided with a knowledge base comprising a set of fuzzyrules describing the fuzzy map between each such output from each saidGLDSs and the location of said leak event, a database defining themembership functions utilized in said fuzzy rules, and a reasoningmechanism arranged for performing inference procedures upon said fuzzyrules, and of said outputs from each of said plurality of GLDSs. Aplurality of first tube SGLDSs is provided for determining, wherein therespective GLDS can be found, the leak event. The SGLDSs are operativelyassociated with inputs of observed changes in said industrial boiler ofat least one SGLSV, and each comprises a corresponding first SGLIE. Eachsuch first SGLIE is provided with a knowledge base comprising a set offuzzy rules describing the fuzzy map between said corresponding SGLSVsand the location in the subgroup of said leak event, a database definingthe membership functions utilized in said fuzzy rules, and a reasoningmechanism arranged for performing inference procedures upon said set offuzzy rules. There is also provided a second SGLIE for receiving anoutput from each of said plurality of SGLDSs and for determining thelocation in the boiler of said leak event. This second SGLIE is providedwith a knowledge base comprising a set of fuzzy rules describing thefuzzy map between each such output from each of said plurality of SGLDSsand the location of said leak event, a database defining the membershipfunctions utilized in said fuzzy rules, and a reasoning mechanismarranged for performing inference procedures upon said fuzzy rules, andof said outputs of each of said plurality of SGLDSs.

104. Still more specifically, operation of this arrangement, in its mostpreferred form comprises determining for said boiler, a set of tubeGLSVs and arranging same into a predetermined number of individualgroups. Each resulting individual group of GLSVs is represented with afuzzy set comprising linguistic statements. For each such individualgroup of GLSVs there is both a knowledge base for a corresponding firstGLILE which contains a set of fuzzy rules describing the fuzzy mapbetween each GLSV in that group and the relative magnitude of said leakevent, and a database for the same corresponding first GLIE whichdefines the membership functions used in the fuzzy rules of thecorresponding group knowledge base with each said corresponding firstGLIE further comprising a reasoning mechanism, said reasoning mechanismdisposed for performing inference procedures upon said fuzzy rules fordecision-making on the magnitude of said leak event. There is alsodetermined for said boiler, a set of tube SGLSVs which are arranged intoa predetermined number of individual subgroups. In the most preferredembodiment, the number of said subgroups is at least equal to the numberof individual groups of SGLSVs, whereby there is at least one individualsubgroup of SGLSVs corresponding to each individual group of GLSVs andwhereby each subgroup comprises at least one SGLSV. Each of theindividual subgroups of SGLSVs is provided with a fuzzy set comprisinglinguistic statements, and for each thereof there is built both aknowledge base for its corresponding first SGLIE which contains a set offuzzy rules describing the fuzzy map between each SGLSV in that subgroupand the location of said leak event, and a database for the samecorresponding SGLIE which defines the membership functions used in thefuzzy rules of the corresponding subgroup knowledge base. Further, eachsuch corresponding SGLIE is provided with a reasoning mechanism forperforming inference procedures upon said fuzzy rules fordecision-making on the location of said leak event. Operation of thearrangement includes monitoring said industrial boiler for theoccurrence of a leak event by observing changes in values in said boilerfor each of said GLSVs in each group, and supplying said observedchanges in values to each first GLIE for generating a fuzzy output fromeach thereof. Each of these resulting fuzzy outputs is introduced to asecond GLIE, said second GLIE being provided with both a knowledge basecomprising a set of fuzzy rules describing the fuzzy map between themagnitude of said leak event and each GLSV and a database definingmembership functions used in the fuzzy rules of said knowledge base,together with a reasoning mechanism for performing inference proceduresupon said fuzzy rules. The resulting fuzzy outputs from the second GLIEis compared to a ranking of the linguistic statements, whereby if any ofthe linguistic statements is greater than a predetermined rank,concluding that a leak event is occurring and further determining inwhich of the individual group of the plurality of GLSV groups said leakevent is located. Subsequently said industrial boiler is monitored forfurther determining the more specific location of said leak event byobserving changes in values from said boiler for each of said SGLSVs,but only those in that particular group identified as containing thesitus of said leak event and supplying said observed changes in valuesto each of the corresponding first SGLIE corresponding to each SGLSV inthat group whereby each such resulting fuzzy output is introduced to asecond SGLIE which is provided with both a knowledge base comprising aset of fuzzy rules describing the fuzzy map between the location of saidleak event and each SGLSV in that group identified by the second GLIE,and a database defining membership functions used in the fuzzy rules insaid knowledge base of said second SGLIE together with a reasoningmechanism for performing inference procedures upon each of said fuzzyrules for determining the location in the boiler of the leak event.

EXAMPLES

105. In order that those skilled in the art may better understand howthe present invention can be practiced, the following examples are givenby way of illustration only and not necessarily by way of limitation,since numerous variations thereof will occur and will undoubtedly bemade by those skilled in the art without substantially departing fromthe true and intended scope of the instant invention herein taught anddisclosed.

Example I

106. In this example, information and data were collected whichcorrespond to a leak which took place in the Kingston 9 boiler of TVA,supra, in the Superheater Intermediate and Pendant (SHIP), a subsystemof the superheater, (Location 4).

107. Referring now more specifically to FIG. 4, the outputs of theeleven LLDSs corresponding to LLDS 1 through LLDS 11, of FIG. 2 supra,are illustrated. The x-axis represents time in terms of points (p). Theactual time is equal to the number of points times five minutes; t=5pminutes. The y-axis represents the leak level as estimated by thedifferent LLDSs in Klb/hr. As previously discussed, five LLDSs had zeroor negligible outputs and five LLDSs had outputs between one and four.LLDS 4 was around ten. Therefore the SHIP was declared as the leaklocation. The outputs of the other LLDSs are treated as false alarms.The decision of the instant, new and novel ANN-based leak detectionsystem was confirmed by later physical inspection of the boiler by TVApersonnel.

Example II

108. Referring now more specifically to FIG. 5, therein is illustratedanother example of a successful ANN based tube leak detection operation.In this case the tube leak took place in June 1996 at TVA Kingston 9boiler. As a result of this leak, a shut down took place in June 19. Ascan be seen in FIG. 5, the instant ANN based tube leak detection systemdetected the beginning of the leak event on June 12. This constitutesabout a seven day early warning period.

INVENTION PARAMETERS

109. After sifting and winnowing through the data, supra, as well asother results and operations of the instant, new, novel, and improvedtechnique, including methods and means for the effecting thereof, theoperating variables, including the acceptable and preferred conditionsfor carrying out this invention are summarized below. Most OperatingPreferred Preferred Variables Limits Limits Limits ULSV 2-6 3-5 4 LLSV10-20 12-18 15 GLSV  6-16  8-12 10

110. The information contained in the table, supra, indicates the numberof sensitive variables needed (desired) by the ANN based detectionsystem. The same number of variables will be applicable for the fuzzylogic based detection system.

111. While we have shown and described particular embodiments of ourinvention, modifications and variations thereof will occur to thoseskilled in the art. We wish it to be understood therefore that theappended claims are intended to cover such modifications and variationswhich are within the true scope and spirit of our invention.

What we claim as new and desire to secure by Letters Patent of theUnited States is:
 1. A process for determining the occurrence andlocation of a boiler tube leak event in industrial boilers, said methodhaving, as its principal object the effecting of such determinationduring the early initiation of such leak event, said method comprising:(a) determining for said boiler, a set of tube universal leak sensitivevariables (ULSV); (b) calibrating the relationship between teaching dataconsisting of a plurality of known output patterns for each of saidULSVs in said set determined in step (a), supra, and learning sampledata consisting of a plurality of sample patterns obtained from actualboiler tube leak events, or simulated boiler tube leak events, or both,by training a universal leak detection system (ULDS) Artificial NeuralNetwork (ANN) by supplying said learning data thereto and comparing samewith the corresponding teaching data patterns to thereby achieve apredetermined degree of convergence towards maximum pattern learningrecognition; (c) determining for said boiler, a set of tube local leaksensitive variables (LLSV); (d) for each individual tube LLSV of the setdetermined in step (c), supra, calibrating the relationship betweenteaching data consisting of a plurality of known output patterns foreach said individual LLSVs and learning sample data consisting of aplurality of sample patterns obtained from actual boiler tube leakevents, or simulated boiler tube leak events, or both, by training, foreach such LLSV, a corresponding ANN by supplying said learning datathereto and comparing same with the corresponding teaching data patternsto thereby achieve a predetermined degree of convergence towards maximumpattern learning recognition; (e) thereafter monitoring said industrialboiler for the occurrence of a leak event by observing changes in valuesin said boiler for each of said ULSVs and supplying said observedchanges in values to said ULDS ANN for calculating a possibility that aleak event is occurring; (f) comparing the possibility calculated instep (e), supra, to a predetermined confidence threshold; (g) if thepossibility compared in step (f), supra, is greater than said confidencethreshold, concluding that a leak event is occurring and thereafterobserving changes in values from said boiler for each of said LLSVs andsupplying said observed changes in values to each of said correspondinglocal leak detection system (LLDS) ANNs for simultaneously calculating apossibility that the leak event is occurring at the locationcorresponding to one of said LLSVs; and (h) comparing each possibilitycalculated by each LLDS ANN in step (g), supra, one with the other, andconcluding from said comparisons the location, in said boiler, of saidleak event.
 2. The process of claim 1 , wherein detecting the occurrenceand location of said leak event is effected during at least onedevelopment stage thereof, wherein the acoustical noise attributablethereto is not significantly greater in the immediate vicinity thereofthan is the background acoustical noise attributable to operation ofsaid boiler.
 3. The process of claim 1 , wherein the determination ofsaid sets of tube ULSVs and tube LLSVs and the training of said ANNs insteps (a)-(d) thereof is effected at a time substantially different fromthe time during which steps (e)-(h) are effected.
 4. The process ofclaim 3 , wherein the time during which steps (e)-(h) are effected, isat least 24 hours subsequent to the time during which said steps (a)-(d)are effected.
 5. The process of claim 1 , wherein the comparisons madein step (h) thereof are effected with an inference engine.
 6. Theprocess of claim 1 , wherein step (g) thereof there are provided elevenLLDS ANNs, each of which corresponds to a location in the boiler whereinit has been predetermined that a leak event is likely to occur, saidlocations comprising: Input LLSV No. Location No. Location in the BoilerLLSV 1 1 Superheater Front Radiant Platen (SHFRP) LLSV 2 2 ReheaterFront Radiant Platen (RHFRP) LLSV 3 3 Superheater Waterwall (SHWW) LLSV4 4 Superheater Intermediate and Pendants (SHIP) LLSV 5 5 SuperheaterOutlet Pendants (SHOP) LLSV 6 6 Reheater Intermediate and Pendants(RHIP) LLSV 7 7 Reheater Outlet Pendants(RHOP) LLSV 8 8 ReheaterWaterwall (RHWW) LLSV 9 9 Primary Superheater (PSH) LLSV 10 10Superheater economizer (SHEC) LLSV 11 11 Reheater Economizer (RHEC)


7. The process of claim 6 , wherein for each of said eleven locationsand associated LLSV, the arrangement of the inputs of the correspondingLLSVs and location numbers (L#) comprise: L#1 L#2 L#3 L#4 L#5 L#6 LLSV 1LLSV 2 LLSV 3 LLSV 4 LLSV 5 LLSV 6 WFH5TOT LFNTLTR (1) LCLW ncfdcs (1)WFH5TOT WFH5TOT PDA PFNGRM PWBS PFNGSM PFNGRM PIPIV ncfdcs (1) PWBSWDIWS PBPGX (11) PWBS LFNTLTR (1) L#7 L#8 L#9 L#10 L#11 LLSV 7 LLSV 8LLSV 9 LLSV 10 LLSV 11 WFH5TOT TBPSI (13) TBPSX (20) LFNTLTS (1) PBPGX(7) PIPIV PHPFSFOX (1) PFNGRM PFNGSM FGSDAI LFNTLTR (1) PBD TBPSX (20)PBPGX (14) TMSDCS


8. The process of claim 7 , wherein twenty LLSVs are utilized andcomprise: VARIABLES MEANING FGSDAI LEAK-OFF HDR FLOW LCLW COLDWELL TANKLEVEL LFNTLTR (1) RH BR TILT CNR #1 POSITION LFNTLTS (1) SH BR TILT CNR#1 POSITION ncfdcs (1) Pulv A Feeder Flow PBD BOILER DRUM PRESS-NORTHPBPGX (11) SH FURNACE PRESS AFTER HT SUPHT PBPGX (14) SH FURNACE PRESSAFTER ECONOMIZER PBPGX (7) RH FURNACE PRESS AFTER ECONOMIZER PDADEAERATOR PRESSURE PFNGRM RH FURNACE DRAFT A PFNGSM SH FURNACCE DRAFT APHPFSFOX (1) TURB FIRST STAGE PRESS A PIPIV HOT REHEAT STEAM INT VLV APRESS PWBS SH FURN WINDBOX PRESSURE TBPSI (13) RH ATTEMPT A BEFORE SPRAYSTM TEMP TBPSX (20) RH OUTLET HEADER TEMPERATURE A TMSDCS SH SEC OUTLETHDR TEMPERATURE A WDIWS COLDWELL TANK MAKEUP FLOW WFH5TOT CONDENSATEFLOW TO DEAERATOR


9. A process for identifying tube leak sensitive variables (LSV)requisite for later determination of the occurrence, the location, orboth, of a boiler tube leak event in an industrial boiler, said processcomprising: (a) collecting changes in values associated with themonitoring of said variables during operation of said boiler; (b)calculating the sensitivity function of each particular variable forwhich changes in values are collected in step (a), supra; (c) comparingeach sensitivity function calculated in step (b), supra, with apredetermined sensitivity threshold, eliminating those variables whosesensitivity function is less than said threshold and collecting thosevariables whose sensitivity function is greater than said threshold; (d)calculating the possibility that one of any of the variables collectedin step (c), supra, contains information redundant with informationcontained in any other of said collected variables; (e) eliminating agiven variable, if the possibility that such variable contains redundantinformation; (f) collecting those variables determined in step (e),supra, to contain information not redundant with information containedin any other variable; (g) comparing the changes in values for each ofthe variables collected in step (f), supra, with standard principles ofthermodynamics and mechanics and eliminating those variables whosechanges in values do not correlate; and (h) collecting for laterdetermination of the occurrence, the location, or both, of said boilertube leak event, those variables which correlate with said standardprinciples in step (g), supra, as tube LSVs.
 10. The process of claim 9, wherein calculating the sensitivity of each particular variable instep (b) thereof quantifies for each, a sensitivity function S(ν_(i))wherein: S(ν_(i))=abs(Δν_(i) /Δl) and further, wherein Δν_(i) representsthe change in the process variable in response to a change in theoccurrence or magnitude, or both, of a tube leak Δl, and abs denotes theabsolute value thereof.
 11. The process of claim 9 , wherein calculatingthe sensitivity of each particular variable in step (b) thereofquantifies for each, a sensitivity function S(ν_(i)) wherein:S(ν_(i))=abs(Δ_(v) _(i) /ν_(i)) and further, wherein Δν_(i) representsthe change in the process variable in response to a tube leak and absdenotes the absolute value thereof.
 12. The process of claim 9 , whereinthe LSV identified is a universal leak sensitive variable (ULSV). 13.The process of claim 9 , wherein the LSV identified is a local leaksensitive variable (LLSV).
 14. The process of claim 9 , wherein the LSVidentified is a group leak sensitive variable (GLSV).
 15. The process ofclaim 9 , wherein the LSV identified is a subgroup leak sensitivevariable (SGLSV).
 16. The process of claim 9 , wherein the LSVidentified is selected from the group consisting of ULSV, LLSV, andmixtures thereof.
 17. The process of claim 12 , wherein about threeseparate ULSVs are identified.
 18. The process of claim 13 , whereinabout twenty separate LLSVs are identified.
 19. The process of claim 14, wherein about eleven separate GLSVs are identified.
 20. The process ofclaim 15 , wherein about eleven separate SGLSVs are identified.
 21. Aprocess for determining the occurrence and location of a boiler tubeleak event in an industrial boiler, said process having, as itsprincipal object the effecting of such determination during the earlyinitiation of such leak event, said process comprising: (a) determiningfor said boiler, a set of tube group leak sensitive variables (GLSV);(b) arranging said set of GLSVS into individual groups; (c) for each ofsaid groups of GLSVs arranged in step (b), supra, calibrating therelationship between teaching data consisting of a plurality of knownoutput patterns of said group and learning sample data consisting of aplurality of sample patterns obtained from actual boiler tube leakevents, simulated boiler tube leak events, or both, by training for eachsuch group of GLSVs, a corresponding Artificial Neural Network (ANN) bysupplying said learning data thereto and comparing same with thecorresponding teaching data patterns to thereby achieve a predetermineddegree of convergence towards maximum pattern recognition; (d)thereafter monitoring said industrial boiler for the occurrence of aleak event by observing changes in values in said boiler for each ofsaid groups of GLSVs and supplying said observed changes in values toeach of said GLSV corresponding ANNs for calculating a possibility thata leak event is occurring; (e) comparing the possibility calculated instep (d), supra, to a predetermined confidence threshold; (f)concluding, if the possibility compared in step (e), supra, is greaterthan said confidence threshold, that a leak event is occurring andthereafter observing changes in values from said boiler for each GLSV ineach said group and supplying said observed changes in values to each ofsaid corresponding GLSV ANNs for simultaneously calculating apossibility that the leak event is occurring at the locationcorresponding to one of said GLSVs in one of said groups; (g) comparingeach possibility calculated by each GLSV ANN in step (f), supra, onewith the other, and concluding from said comparison the location in saidboiler of the group in which said leak event is occurring; (h)determining for said boiler a set of tube subgroup leak sensitivevariables (SGLSV); (i) arranging said set of tube SGLSVs into individualsubgroups; (j) for each of said subgroups of SGLSVs arranged in step(i), supra, calibrating the relationship between teaching dataconsisting of a plurality of known output patterns of said subgroup andlearning sample data consisting of a plurality of sample patternsobtained from actual boiler tube leak events, simulated boiler tube leakevents, or both, by training for each such SGLSV, a correspondingArtificial Neural Network (ANN) by supplying said learning data theretoand comparing same with the corresponding teaching data patterns tothereby achieve a predetermined degree of convergence towards maximumpattern recognition; (k) observing changes in values in said boiler foreach SGLSV associated with the subgroup identified in step (g), supra,and supplying said observed changes in values to said SGLSVcorresponding ANN trained in step (j), supra, for calculating apossibility that the leak event is occurring at the locationcorresponding to one of said SGLSVs; and (l) comparing each possibilitycalculated by the SGLSV ANN in step (k), supra, one with the other, andconcluding from said comparison the location in said boiler of said leakevent.
 22. The process of claim 21 , wherein the occurrence and locationof said leak event is effected during at least one development stagethereof wherein the acoustical noise attributable thereto is notsignificantly greater in the immediate vicinity thereof than is thebackground acoustical noise attributable to operation of said boiler.23. The process of claim 21 , wherein the determination of said set oftube GLSVs and the training of said ANNs in steps (a)-(c) and steps (h)and (i) thereof is effected at a time substantially different from thetime during which steps (d)-(g) and steps (k) and (l) are effected. 24.The process of claim 23 , wherein said steps (d)-(g) and (k) and (l) areeffected at least 24 hours subsequent to the time wherein steps (a)-(c)and steps (h) and (j) are effected.
 25. The process of claim 24 ,wherein the occurrence and location of said tube leak event is effectedduring at least one development stage thereof, wherein the acousticalnoise attributable thereto is not significantly greater in the immediatevicinity thereof than is the background acoustical noise attributable tooperation of said boiler.
 26. The process of claim 24 , wherein theconclusions made in steps (h) and (k) thereof are effected with aninference engine.
 27. The process of claim 22 , wherein step (f) thereofthere are provided four group leak detection system (GLDS) ANNs, each ofwhich said GLDS ANNs determines the likelihood of a leak event in eachgroup in the boiler wherein a leak event is likely to occur, saidlocations comprising the economizer, the waterwall, the superheater, andthe reheater.
 28. The process of claim 27 , wherein for each of saidfour GLDSs, the arrangement of corresponding GLSVs and associatedvariables comprise: GLSV 1 GLSV 2 GLSV 3 GLSV 4 (Economizer) (Waterwall)(Superheater) (Reheater) WDIWS PBPGX (14) PPRX (2) PBPGX (6) PDA LFNTLTS(1) PBPGX (11) PBPGX (4) PBPGX (14) PBPGX (14) PBPGX (7) FFD (1) CHWMUVO


29. The process of claim 28 , wherein eleven GLSVs are identified andcomprise: VARIABLE MEANING PPRX (2) ID Fan Pressure PBPGX (11) SHFurnace Pressure after HT Supht PBPGX (14) SH Furnace Pressure afterEconomizer FFD (1) Combustion Air Flow WDIWS Coldwell Tank Makeup FlowPDA De-aerator Pressure LFNTLTS (1) SH BR Tilt CNR #1 Position PBPGX (6)Reheater Furnace Pressure after LT SHTR PBPGX (4) Reheat FurnacePressure After Reheater PBPGX (7) Reheat Furnace Pressure AfterEconomizer CHWMUVO Hotwell Make up Valve Demand


30. A system for identifying tube leak sensitive variables (LSV)requisite for later determination of the occurrence, the location, orboth, of a boiler tube leak event in an industrial boiler, said systemcomprising: (a) first information collection means for storing changesin values associated with the monitoring of said LSVs during operationof said boiler; (b) first calculating means for determining thesensitivity function of each particular LSV for which changes in valuesare assembled in said first collection means; (c) first comparing meansfor matching the sensitivity function calculated in said firstcalculating means with a predetermined sensitivity function thresholdand identifying those variables whose sensitivity function is greaterthan said threshold function; (d) second information collection meansfor assembly of those LSVs identified in said first comparing means ashaving a sensitivity function greater than said threshold function; (e)second calculating means for determining the possibility that any one ofthe LSVs assembled in said second information collection means isredundant with information contained in any other of said LSVs assembledtherein; (f) third information collection means for assembly of thoseLSVs determined in said second calculating means to contain informationnot redundant with information contained in any other of such LSVs; (g)second comparing means for matching observed changes in values for eachof the LSVs collected in said third information collection means withstandard principles of thermodynamics and mechanics; and (h) fourthinformation collection means for assembly of the resulting tube LSVsidentified in said second comparing means as correlating with saidstandard principles.
 31. The system of claim 30 , wherein saidsensitivity function determined in said first calculating means for eachparticular LSV is quantified as S(ν_(i)) wherein: S(ν_(i))=abs(Δν_(i)/Δl) and further, wherein Δν_(i) represents the change in the processvariable in response to a change in the occurrence and/or magnitude of atube leak Δl and abs denotes the absolute value thereof.
 32. The systemof claim 30 , wherein said sensitivity function determined in said firstcalculating means for each particular LSV is quantified as S(ν_(i))wherein: S(ν_(i))=abs(Δ_(ν) _(i) /ν_(i)) and further, wherein Δν_(i)represents the change in the process variable in response to a tube leakand abs denotes the absolute value thereof.
 33. A system for determiningthe occurrence and location of a boiler tube leak event in an industrialboiler, said system comprising: (a) tube universal leak detection system(ULDS) means for determining the likelihood of an occurrence of a tubeleak event, said ULDS means operatively associated with inputs ofobserved changes in said industrial boiler of universal leak sensitivevariables (ULSV), and comprising a first Artificial Neural Network (ANN)trained on the desired convergence between ULSV teaching data, and ULSVlearning data; (b) a plurality of local leak detection system (LLDS)means, each of which is operatively associated with inputs of one of aplurality of sets of observed changes in said industrial boiler of localleak sensitive variables (LLSV) each of said LLDS means comprising acorresponding second ANN trained on the desired convergence between LLSVteaching data and LLSV learning data; and (c) inference engine means forreceiving an output from each of said plurality of sets of LLDS meansand for determining the location in the boiler of said leak event. 34.The system of claim 33 , wherein said ULSV teaching data consists of aplurality of known output patterns of a plurality of ULSVs and said ULSVlearning data consists of a plurality of sample patterns obtained fromactual boiler tube leak events, simulated boiler tube leak events, orboth, and wherein said LLSV teaching data consists of a plurality ofknown output patterns of individual LLSVs and said LLSV learning dataconsists of a plurality of sample patterns obtained from actual boilertube leak events, simulated boiler tube leak events, or both.
 35. Thesystem of claim 34 , wherein there are provided at least one first ANNand about eleven of said corresponding second ANNs.
 36. The system ofclaim 35 , wherein there are provided about three inputs of said ULSVsto said first ANN.
 37. The system of claim 36 , wherein three inputs ofsaid ULSVs comprise: VARIABLES MEANING WDIWS COLDWELL TANK MAKEUP FLOWFFD (2) COMBUSTION AIR FLOW A1 PPRX (2) ID FAN A INLET SUCTION PRESS


38. The system of claim 35 , wherein there are provided about twentyinputs of said LLSVs to eleven corresponding second ANNs.
 39. The systemof claim 38 , wherein said about twenty inputs of said LLSVs comprise:VARIABLES MEANING FGSDAI LEAK-OFF HDR FLOW LCLW COLDWELL TANK LEVELLFNTLTR (1) RH BR TILT CNR #1 POSITION LFNTLTS (1) SH BR TILT CNR #1POSITION ncfdcs (1) Pulv A Feeder Flow PBD BOILER DRUM PRESS-NORTH PBPGX(11) SH FURNACE PRESS AFTER HT SUPHT PBPGX (14) SH FURNACE PRESS AFTERECONOMIZER PBPGX (7) RH FURNACE PRESS AFTER ECONOMIZER PDA DEAERATORPRESSURE PFNGRM RH FURNACE DRAFT A PFNGSM SH FURNACCE DRAFT A PHPFSFOX(1) TURB FIRST STAGE PRESS A PIPIV HOT REHEAT STEAM INT VLV A PRESS PWBSSH FURN WINDBOX PRESSURE TBPSI (13) RH ATTEMPT A BEFORE SPRAY STM TEMPTBPSX (20) RH OUTLET HEADER TEMPERATURE A TMSDCS SH SEC OUTLET HDRTEMPERATURE A WDIWS COLDWELL TANK MAKEUP FLOW WFH5TOT CONDENSATE FLOW TODEAERATOR


40. The system of claim 39 , wherein said at least first ANN and saideleven corresponding second ANNs each are comprised of one input layerprovided with about three neurons, a first hidden layer provided withabout forty neurons, a second hidden layer provided with abouttwenty-four neurons, and an output layer provided with at least oneneuron.
 41. A system for determining the occurrence and location of aboiler tube leak event in an industrial boiler, said system comprising:(a) a first plurality of first tube group leak detection system (GLDS)means for determining the likelihood of the occurrence of a tube leakevent, each of said GLDS means operatively associated with inputs ofobserved changes in said industrial boiler of group leak sensitivevariables (GLSV) and comprising a plurality of corresponding firstArtificial Neural Networks (ANN) trained on the desired convergencebetween GLSV teaching data, and GLSV learning data, and operativelyassociated with inputs of observed changes in said industrial boiler ofsaid GLSVs; (b) first inference engine means for receiving an outputfrom each of said plurality of GLDS means and for determining the groupin which the boiler leak event is occurring; (c) a plurality of tubesubgroup leak detection system (SGLDS) means for determining thelikelihood in the group identified by said first inference engine meansof the location of said leak event, each of which is operativelyassociated with inputs of observed changes in said industrial boiler ofsubgroup leak sensitive variables (SGLSV) each of said SGLDS meanscomprising a corresponding second ANN trained on the desired convergencebetween SGLSV teaching data, and SGLSV learning data; and (d) secondinference engine means for receiving an output from each of saidplurality of SGLDS means and for determining the location in the boilerof said leak event.
 42. The system of claim 41 , wherein said GLSVteaching data consists of a plurality of known output patterns of aplurality of GLSVs and said GLSV learning data consists of a pluralityof sample patterns obtained from actual boiler tube leak events,simulated boiler tube leak events, or both, and wherein said SGLSVteaching data consists of a plurality of known output patterns ofindividual SGLSVs and said SGLSV learning data consists of a pluralityof sample patterns obtained from actual boiler tube leak events,simulated boiler tube leak events, or both.
 43. The system of claim 42 ,wherein there are provided about four corresponding first ANNs and aboutsix corresponding second ANNs.
 44. The system of claim 43 , whereinthere are provided to each of said corresponding first ANNs about fourinputs of said GLSVs.
 45. The system of claim 44 , wherein anarrangement of four inputs to said plurality of corresponding first ANNsassociated with each of four groups of GLSVs is: GLSV 1 GLSV 2 GLSV 3GLSV 4 (Economizer) (Waterwall) (Superheater) (Reheater) WDIWS PBPGX(14) PPRX (2) PBPGX (6) PDA LFNTLTS (1) PBPGX (11) PBPGX (4) PBPGX (14)PBPGX (14) PBPGX (7) FFD (1) CFWMUVO


46. The system of claim 45 , wherein eleven GLSVs are identified andcomprise: VARIABLE MEANING PPRX (2) ID Fan Pressure PBPGX (11) SHFurnace Pressure after HT Supht PBPGX (14) SH Furnace Pressure afterEconomizer FFD (1) Combustion Air Flow WDIWS Coldwell Tank Makeup FlowPDA De-aerator Pressure LFNTLTS (1) SH BR Tilt CNR #1 Position PBPGX (6)Reheater Furnace Pressure after LT SHTR PBPGX (4) Reheat FurnacePressure After Reheater PBPGX (7) Reheat Furnace Pressure AfterEconomizer CHWMUVO Hotwell Make up Valve Demand


47. The system of claim 43 , wherein there are provided to each of saidcorresponding second ANNs about six inputs of said SGLSVs.
 48. A processfor determining the occurrence and location of a boiler tube leak eventin industrial boilers, said process having, as its principal object theeffecting of such determination during the early initiation of such leakevent, said process comprising: (a) determining for said boiler, a setof tube universal leak sensitive variables (ULSV); (b) representing eachof said ULSVs with a fuzzy set comprising linguistic statements; (c)building a knowledge base for a first inference engine which contains aset of fuzzy rules describing the fuzzy map between each of said ULSVsand the relative magnitude of said leak event; (d) building a databasefor said first inference engine which defines the membership functionsused in the fuzzy rules of said knowledge base, and said first inferenceengine further comprising reasoning mechanism for performing inferenceprocedures upon said set of fuzzy rules for decision-making on themagnitude of said leak event; (e) determining for said boiler, a set oftube local leak sensitive variables (LLSV); (f) representing each ofsaid LLSVs with a fuzzy set comprising linguistic statements; (g)building a knowledge base for each of a set of second inference engines,each said second inference engine in said set corresponding to one ofsaid LLSVs, and each such knowledge base comprising a set of fuzzy rulesdescribing the fuzzy map between that LLSV corresponding to that secondinference engine and the location of said leak event; (h) for each ofsaid second inference engines for which a knowledge base is built instep (g), supra, building a database which defines the membershipfunctions used in the fuzzy rules with the knowledge base correspondingto said second inference engine and said second inference enginesfurther comprising reasoning mechanism for performing inferenceprocedures upon its corresponding set of fuzzy rules for decision-makingon the location of said leak event; (i) thereafter monitoring saidindustrial boiler for the occurrence of a leak event by observingchanges in values in said boiler for each of said ULSVs and supplyingsaid observed changes in values to said first inference engine forgenerating a fuzzy output therefrom; (j) comparing the fuzzy output instep (i), supra, to a ranking of the linguistic statements representedin step (b), supra; (k) if the linguistic statement compared in step (j)supra, is greater than a predetermined rank, concluding that a leakevent is occurring and thereafter observing changes in values from saidboiler for each of said LLSVs and supplying said observed changes invalues to said LLSVs corresponding second inference engine forsimultaneously producing therefrom a fuzzy output; and (l)simultaneously introducing each fuzzy output produced in step (k),supra, to a third inference engine, said third inference engine beingprovided with a knowledge base comprising a set of fuzzy rulesdescribing the fuzzy map between the location of said leak event andeach LLSV, a database defining membership functions used in the fuzzyrules of the third inference engine knowledge base, and reasoningmechanism for performing inference procedures upon each of said fuzzyrules for determining the location, in said boiler, of said leak event.49. The process of claim 48 , wherein detecting the occurrence andlocation of said leak event is effected during at least one developmentstage thereof wherein the acoustical noise attributable thereto is notsignificantly greater in the immediate vicinity thereof than is thebackground acoustical noise attributable to operation of said boiler.50. The process of claim 48 , wherein, the determination of said sets oftube ULSVs and tube LLSVs and the building of said knowledge bases andsaid databases in steps (a)-(h) thereof is effected at a timesubstantially different from the time of monitoring and determining theoccurrence and location of a leak event and during which steps (i)-(l)are effected.
 51. The process of claim 50 , wherein the time duringwhich steps (i)-(l) are effected, is at least 24 hours subsequent to thetime during which said steps (a)-(h) are effected.
 52. The process ofclaim 48 , wherein step (e) thereof, there are determined for said setof eleven LLSVs, each of which corresponds to a location in the boilerwherein it has been predetermined that a leak event is likely to occur,said locations comprising: Input LLSV No. Location No. Location in theBoiler LLSV 1 1 Superheater Front Radiant Platen (SHFRP) LLSV 2 2Reheater Front Radiant Platen (RHFRP) LLSV 3 3 Superheater Waterwall(SHWW) LLSV 4 4 Superheater Intermediate and Pendants (SHIP) LLSV 5 5Superheater Outlet Pendants (SHOP) LLSV 6 6 Reheater Intermediate andPendants (RHIP) LLSV 7 7 Reheater Outlet Pendants(RHOP) LLSV 8 8Reheater Waterwall (RHWW) LLSV 9 9 Primary Superheater (PSH) LLSV 10 10Superheater economizer (SHEC) LLSV 11 11 Reheater Economizer (RHEC)


53. The process of claim 52 , wherein for each of said eleven locationsand associated LLSVs, the arrangement of the inputs of the correspondingLLSVs and location numbers (L#) comprise: L#1 L#2 L#3 L#4 L#5 L#6 LLSV 1LLSV 2 LLSV 3 LLSV 4 LLSV 5 LLSV 6 WFH5TOT LFNTLTR (1) LCLW ncfdcs (1)WFH5TOT WFH5TOT PDA PFNGRM PWBS PFNGSM PFNGRM PIPIV ncfdcs (1) PWBSWDIWS PBPGX (11) PWBS LFNTLTR (1) L#7 L#8 L#9 L#10 L#11 LLSV 7 LLSV 8LLSV 9 LLSV 10 LLSV 11 WFH5TOT TBPSI (13) TBPSX (20) LFNTLTS (1) PBPGX(7) PIPIV PHPFSFOX (1) PFNGRM PFNGSM FGSDAI LFNTLTR (1) PBD TBPSX (20)PBPGX (14) TMSDCS


54. The process of claim 53 , wherein twenty LLSVs are utilized andcomprise: VARIABLES MEANING FGSDAI LEAK-OFF HDR FLOW LCLW COLDWELL TANKLEVEL LFNTLTR (1) RH BR TILT CNR #1 POSITION LFNTLTS (1) SH BR TILT CNR#1 POSITION ncfdcs (1) Pulv A Feeder Flow PBD BOILER DRUM PRESS-NORTHPBPGX (11) SH FURNACE PRESS AFTER HT SUPHT PBPGX (14) SH FURNACE PRESSAFTER ECONOMIZER PBPGX (7) RH FURNACE PRESS AFTER ECONOMIZER PDADEAERATOR PRESSURE PFNGRM RH FURNACE DRAFT A PFNGSM SH FURNACCE DRAFT APHPFSFOX (1) TURB FIRST STAGE PRESS A PIPIV HOT REHEAT STEAM INT VLV APRESS PWBS SH FURN WINDBOX PRESSURE TBPSI 13) RH ATTEMPT A BEFORE SPRAYSTM TEMP TBPSX (20) RH OUTLET HEADER TEMPERATURE A TMSDCS SH SEC OUTLETHDR TEMPERATURE A WDIWS COLDWELL TANK MAKEUP FLOW WFH5TOT CONDENSATEFLOW TO DEAERATOR


55. A process for determining the occurrence and location of a boilertube leak event in an industrial boiler, said process having, as itsprincipal object the effecting of such determination during the earlyinitiation of such leak event, said process comprising: (a) determiningfor said boiler, a set of tube group leak sensitive variables (GLSV);(b) arranging said set of GLSVs into a predetermined number ofindividual groups; (c) representing each individual group of GLSVsarranged in step (b), supra, with a fuzzy set comprising linguisticstatements; (d) for each of said individual groups of GLSVs arranged instep (b), supra, building a knowledge base for a corresponding firstgroup leak inference engine (GLIE) which contains a set of fuzzy rulesdescribing the fuzzy map between each GLSV in that group and therelative magnitude of said leak event; (e) for each of said individualgroups of GLIEs for which a knowledge base is built in step (d), supra,building a database for the same corresponding first GLIE which definesthe membership functions used in the fuzzy rules of the correspondinggroup knowledge base and said corresponding first GLIE furthercomprising a reasoning mechanism, said reasoning mechanism disposed forperforming inference procedures upon said fuzzy rules fordecision-making on the magnitude of said leak event; (f) determining forsaid boiler a set of tube subgroup leak sensitive variables (SGLSV); (g)arranging said set of SGLSVs into a predetermined number of individualsubgroups, said predetermined number being at least equal to thepredetermined number of individual groups arranged in step (b), supra,whereby there is at least one individual subgroup of SGLSVscorresponding to each individual group of GLSVs and whereby eachsubgroup comprises at least one SGLSV; (h) representing each individualsubgroup of SGLSVs arranged in step (g), supra, with a fuzzy setcomprising linguistic statements; (i) for each of said individualsubgroups of SGLSVs arranged in step (g), supra, building a knowledgebase for a corresponding first subgroup leak inference engine (SGLIE)which contains a set of fuzzy rules describing the fuzzy map betweeneach SGLSV in that subgroup and the location of said leak event; (j) foreach of said individual subgroups of SGLIEs for which a knowledge baseis built in step (i), supra, building a database for the samecorresponding SGLIE which defines the membership functions used in thefuzzy rules of the corresponding subgroup knowledge base, and saidcorresponding SGLIE further comprising a reasoning mechanism forperforming inference procedures upon said fuzzy rules fordecision-making on the location of said leak event; (k) thereaftermonitoring said industrial boiler for the occurrence of a leak event byobserving changes in values in said boiler for each of said GLSVs ineach group arranged in step (b), supra, and supplying said observedchanges in values to each of said corresponding first group inferenceengines for generating a fuzzy output from each thereof; (l)simultaneously introducing each fuzzy output produced in step (k),supra, to a second GLIE, said second GLIE provided with a knowledge basecomprising a set of fuzzy rules describing the fuzzy map between themagnitude of said leak event and each GLSV in the set arranged in step(b), supra, a database defining membership functions used in the fuzzyrules of said knowledge base, and reasoning mechanism for performinginference procedures upon said fuzzy rules for comparing the fuzzyoutputs in step (k), supra, to a ranking of the linguistic statementsrepresented in step (c), supra, whereby if any of the linguisticstatements is greater than a predetermined rank, concluding that a leakevent is occurring and further concluding in which of the individualgroups comprising the set arranged in step (b), supra, said leak eventis located; (m) thereafter monitoring said industrial boiler for furtherdetermining the location of said leak event by observing changes invalues from said boiler for each of said SGLSVs corresponding to thatindividual group identified in step (1), supra, as containing the situsof said leak event and supplying said observed changes in values to eachof said corresponding first SGLIEs for generating a fuzzy output fromeach thereof; (n) simultaneously introducing each fuzzy output producedin step (m), supra, to a second SGLIE provided with a knowledge basecomprising a set of fuzzy rules describing the fuzzy map between thelocation of said leak event and each SGLSV in that group identified instep (l), supra, a database defining membership functions used in thefuzzy rules in said knowledge base of said second SGLIE and reasoningmechanism for performing inference procedures upon each of said fuzzyrules for determining the location in said boiler of said leak event.56. The process of claim 55 , wherein the occurrence and location ofsaid leak event is effected during at least one development stagethereof wherein the acoustical noise attributable thereto is notsignificantly greater in the immediate vicinity thereof than is thebackground acoustical noise attributable to operation of said boiler.57. The process of claim 55 , wherein the determination of said set oftube GLSVs and SGLSvs and the building of said knowledge bases and saiddatabases in steps (a)-(j) thereof is effected at a time substantiallydifferent from the time during which steps (k) and (m) are effected. 58.The process of claim 57 , wherein said steps (k)-(m) are effected atleast 24 hours subsequent to the time wherein steps (a)-(j) areeffected.
 59. The process of claim 55 , wherein step (b) thereof thereare arranged four individual groups for each of said four GLDSs andfurther, wherein the arrangement of corresponding GLSVs and associatedvariables comprise: GLSV 1 GLSV 2 GLSV 3 GLSV 4 (Economizer) (Waterwall)(Superheater) (Reheater) WDIWS PBPGX (14) PPRX (2) PBPGX (6) PDA LFNTLTS(1) PBPGX (11) PBPGX (4) PBPGX (14) PBPGX (14) PBPGX (7) FFD (1) CHWMUVO


60. The process of claim 59 , wherein eleven GLSVs are determined insaid set and comprise: VARIABLE MEANING PPRX (2) ID Fan Pressure PBPGX(11) SH Furnace Pressure after HT Supht PBPGX (14) SH Furnace Pressureafter Economizer FFD (1) Combustion Air Flow WDIWS Coldwell Tank MakeupFlow PDA De-aerator Pressure LFNTLTS (1) SH BR Tilt CNR #1 PositionPBPGX (6) Reheater Furnace Pressure after LT SHTR PBPGX (4) ReheatFurnace Pressure After Reheater PBPGX (7) Reheat Furnace Pressure AfterEconomizer CHWMUVO Hotwell Make up Valve Demand


61. A system for determining the occurrence and location of a boilertube leak event in an industrial boiler, said system comprising: (a)tube universal leak detection system (ULDS) means for determining thelikelihood of an occurrence of a tube leak event, said ULDS meansoperatively associated with inputs of observed changes in saidindustrial boiler of universal leak sensitive variables (ULSV), andcomprising a first inference engine, said first inference engineprovided with a knowledge base comprising a set of fuzzy rulesdescribing the fuzzy map between each of said ULSVs and the relativemagnitude of said leak event, a database defining the membershipfunctions utilized in said fuzzy rules, and reasoning mechanism arrangedfor performing inference procedures upon said set of fuzzy rules; (b) aplurality of local leak detection system (LLDS) means, each of which isoperatively associated with inputs of one of a plurality of observedchanges in said industrial boiler of local leak sensitive variables(LLSV) each of said LLDS means comprising a corresponding secondinference engine, each said second inference engine provided with aknowledge base comprising a set of fuzzy rules describing the fuzzy mapbetween the corresponding LLSV and the location of said leak event, adatabase defining the membership functions utilized in said fuzzy rules,and reasoning mechanism arranged for performing inference proceduresupon said fuzzy rules, said membership functions; and (c) thirdinference engine means for receiving an output from each of saidplurality of LLDS means and for determining the location in the boilerof said leak event, said third inference engine provided with aknowledge base comprising a set of fuzzy rules describing the fuzzy mapbetween each of said LLDS means and the location of said leak event, adatabase defining the membership functions utilized in said fuzzy rules,and reasoning mechanism arranged for performing inference proceduresupon said fuzzy rules, and of the output from each of said plurality ofLLDS means.
 62. The system of claim 61 , wherein there are provided atleast one first inference engine and about eleven of said correspondingsecond inference engines.
 63. The system of claim 62 , wherein there areprovided about three inputs of said ULSVs to said first inferenceengine.
 64. The system of claim 63 , wherein three inputs of said ULSVscomprise: VARIABLES MEANING WDIWS COLDWELL TANK MAKEUP FLOW FFD (2)COMBUSTION AIR FLOW A1 PPRX (2) ID FAN A INLET SUCTION PRESS


65. The system of claim 64 , wherein there are provided about twentyinputs of said LLSVs to eleven corresponding second inference engines.66. The system of claim 65 , wherein said about twenty inputs of saidLLSVs comprise: VARIABLES MEANING FGSDAI LEAK-OFF HDR FLOW LCLW COLDWELLTANK LEVEL LFNTLTR (1) RH BR TILT CNR #1 POSITION LFNTLTS (1) SH BR TILTCNR #1 POSITION ncfdcs (1) Pulv A Feeder Flow PBD BOILER DRUMPRESS-NORTH PBPGX (11) SH FURNACE PRESS AFTER HT SUPHT PBPGX (14) SHFURNACE PRESS AFTER ECONOMIZER PBPGX (7) RH FURNACE PRESS AFTERECONOMIZER PDA DEAERATOR PRESSURE PFNGRM RH FURNACE DRAFT A PFNGSM SHFURNACCE DRAFT A PHPFSFOX (1) TURB FIRST STAGE PRESS A PIPIV HOT REHEATSTEAM INT VLV A PRESS PWBS SH FURN WINDBOX PRESSURE TBPSI (13) RHATTEMPT A BEFORE SPRAY STM TEMP TBPSX (20) RH OUTLET HEADER TEMPERATUREA TMSDCS SH SEC OUTLET HDR TEMPERATURE A WDIWS COLDWELL TANK MAKEUP FLOWWFH5TOT CONDENSATE FLOW TO DEAERATOR


67. A system for determining the occurrence and location of a boilertube leak event in an industrial boiler, said system comprising: (a) aplurality of first tube group leak detection system (GLDS) means fordetermining the likelihood of the occurrence of a tube leak event, eachof said GLDS means operatively associated with inputs of observedchanges in said industrial boiler of at least one corresponding groupleak sensitive variable (GLSV) and comprising a corresponding firstgroup leak inference engine (GLIE), each said first GLIE provided with aknowledge base comprising a set of fuzzy rules describing the fuzzy mapbetween said at least one corresponding GLSV and the relative magnitudeand group location of said leak event, a database defining themembership functions utilized in said fuzzy rules, and reasoningmechanism arranged for performing inference procedures upon said set offuzzy rules; (b) second GLIE engine means for receiving an output fromeach of said plurality of GLDS means and for determining the likelihoodof a leak event and the corresponding GLDS by which such boiler leakevent is represented, said second GLIE engine provided with a knowledgebase comprising a set of fuzzy rules describing the fuzzy map betweeneach such output from each said GLDS means and the location of said leakevent, a database defining the membership functions utilized in saidfuzzy rules, and reasoning mechanism arranged for performing inferenceprocedures upon said fuzzy rules, and of said outputs from each of saidplurality of GLDSs; (c) a plurality of first tube subgroup leakdetection system (SGLDS) means for determining in the GLDS representedin step (b), supra, the location of said leak event, said SGLDSsoperatively associated with inputs of observed changes in saidindustrial boiler of at least one subgroup leak sensitive variable(SGLSV), and comprising a corresponding first SGLIE, said first SGLIEengine provided with a knowledge base comprising a set of fuzzy rulesdescribing the fuzzy map between said corresponding SGLSVs and thesubgroup location of said leak event, a database defining the membershipfunctions utilized in said fuzzy rules, and reasoning mechanism arrangedfor performing inference procedures upon said set of fuzzy rules; and(d) second subgroup inference engine means for receiving an output fromeach of said plurality of SGLDS means and for determining the locationin the boiler of said leak event, said second subgroup inference engineprovided with a knowledge base comprising a set of fuzzy rulesdescribing the fuzzy map between each such output from each of saidplurality of SGLDS means and the location of said leak event, a databasedefining the membership functions utilized in said fuzzy rules, andreasoning mechanism arranged for performing inference procedures uponsaid fuzzy rules, and of said outputs of each of said plurality ofSGLDSs.
 68. The system of claim 67 , wherein there are provided aboutfour GLDSs and about six SGLDSs.
 69. The system of claim 68 , whereinthere are provided to each of said GLDSs from about two to about fourinputs of said GLSVs.
 70. The system of claim 69 , wherein anarrangement of inputs to said GLDSs associated with each of group ofGLSVs is: GLSV 1 GLSV 2 GLSV 3 GLSV 4 (Economizer) (Waterwall)(Superheater) (Reheater) WDIWS PBPGX (14) PPRX (2) PBPGX (6) PDA LFNTLTS(1) PBPGX (11) PBPGX (4) PBPGX (14) PBPGX (14) PBPGX (7) FFD (1) CHWMUVO


71. The system of claim 70 , wherein eleven GLSVs are identified andcomprise: VARIABLE MEANING PPRX (2) ID Fan Pressure PBPGX (11) SHFurnace Pressure after HT Supht PBPGX (14) SH Furnace Pressure afterEconomizer FFD (1) Combustion Air Flow WDIWS Coldwell Tank Makeup FlowPDA De-aerator Pressure LFNTLTS (1) SH BR Tilt CNR #1 Position PBPGX (6)Reheater Furnace Pressure after LT SHTR PBPGX (4) Reheat FurnacePressure After Reheater PBPGX (7) Reheat Furnace Pressure AfterEconomizer CHWMUVO Hotwell Make up Valve Demand


72. The system of claim 68 , wherein the arrangement of six inputs tosaid plurality of second subgroup inference engines associated with saidsubgroups of SGLDSs is: SGLSV 11 SGLSV 12 SGLSV 2 SGLSV 3 SGLSV 41 SGLSV42 PBPGX (14) PBPGX (13) WDIWS PBPGX (14) PBPGX (6) PBPGX (6) PBPGX (13)PBPGX (11) PDA LFNTLTS (1) PBPGX (4) PBPGX (7) FFD (1) PBPGX (14)CHWMUVO PPRX (2)